IEEE Geek Life

Diverse ämnen från IEEE

1. United States Invests in Industry Partnerships for Ph.D. Training

   

   More than 74 percent of engineering doctoral graduates in the United States in 2024 went into industry, according to the most recent data from the U.S. National Science Foundation’s Survey of Earned Doctorates. The percentage is smaller across all science and engineering fields, but more than half of these graduates have pursued industry positions since 2019. Despite this reality, academia and industry often operate in their own bubbles, and even highly skilled doctoral students may not be immediately prepared to work at a company.

   A new NSF-funded pilot program aims to help bridge the gap. Announced in late July, the Industry-Integrated Ph.D. Scholar Program, or I-PhD, will integrate industry experience into STEM doctoral training with partnerships between individual companies and universities. The pilot includes four years of funding for 250 students, beginning in the 2026–27 academic year, and will be led by the University-Industry Demonstration Partnership (UIDP), a nonprofit that identifies issues affecting relationships between academia and industry in order to improve collaboration.

   A New Ph.D. Funding Structure

   The idea for the program started around January 2021, says Anthony Boccanfuso, president and CEO of UIDP. During a town hall, industry members told the organization that supporting Ph.D. students in the United States was not as appealing as it was in other countries, where industry Ph.D.s are already an option. The program draws on those similar models in Canada, the European Union, the United Kingdom, and elsewhere. It’s not intended as a replacement for existing Ph.D. pathways but as an additional option, Boccanfuso says.

   “We created a program that increases the [return on investment] for companies by reducing the investment that they have to make financially, but increasing their investment in nonfinancial ways, by having them mentor a student who spends a year at their site,” Boccanfuso says. At launch, 30 university-industry partnerships had submitted letters of interest—the first step toward a formal application—and he says many more have since expressed interest in joining.

   In this new model, the first year of a graduate student’s study is funded by the university. Then, for years two through four, funding comes from both the industry partner and the NSF. The federal agency will invest a total of US $47 million over the five-year pilot.

   The announcement comes amidst a slew of recent funding changes at the NSF: The agency has clawed back around $500 million of already distributed funding for some research, Nature reported in July, while it invests heavily in other initiatives such as independent research organizations it calls “X-Labs.” According to a 17 August press release, the NSF announced another $1.5 billion in funding opportunities for foundational research across science and engineering, while it is reportedly set to issue the lowest number of new grants in four decades.

   NSF Program Requirements and Flexibility

   To participate, students must complete at least one year (in aggregate) of their dissertation research at an industry site; universities must fund the first year of study; and industry partners must invest $100,000 per student. The program also includes structured professional development, and UIDP is creating a standardized certificate for industry preparedness.

   But beyond these key requirements, UIDP won’t determine many details. For instance, universities and companies are responsible for forming partnerships, selecting students, and determining projects. Intellectual property rights and publication policies are also handled case by case by each partnership.

   “Computing engineering thinks differently about intellectual property than biomedical engineering,” Boccanfuso says, noting that many companies already have research agreements that outline how to handle IP. “The [research] community is really smart. Let them figure out what works for their situation.”

   Although students can continue their studies longer, I-PhD only includes four years of funding. Beyond that, universities are responsible for funding students, and most U.S. STEM students take five years or longer to complete a Ph.D.

   “I’m curious if this signals to students that four years is the amount of time the program should take, and if it is really enough time,” says Gabriella Coloyan Fleming, founder of Oerlikon Research & Consulting, a research-based consulting firm focused on bridging the gap between STEM industry and education through workforce strategy and educational-program design. One of the main goals of a Ph.D. is to learn research skills, she says, so she would want to make sure that this remains a focus as industry stakeholders become involved in student training. “How can you make sure that the Ph.D. is still a research degree, rather than work experience?”

   Boccanfuso acknowledges that Ph.D. length is a “contentious issue,” but says that it’s outside of UIDP’s scope.

   Changing Mindsets in Academia

   Apart from its length, Fleming says the pilot could also signal to students that working in industry is a valid option. Even though most STEM Ph.D. graduates end up in industry jobs, “the industry career pathway can be a taboo topic in academia—sometimes professors think it’s academia or bust,” she says. The program could help break the barrier and provide perspectives and mentorship students might not otherwise have access to.

   Professors who are already involved in university programs with industry partners are also excited about the pilot’s potential as a new option for students.

   “This is a welcome addition to the federal funding landscape for research,” says Diana Marculescu, chair of the electrical and computer engineering department at the University of Texas at Austin. “I’m really interested to see more, and I’m looking forward to seeing how we can participate.”

   At UT Austin, partnerships with local companies are already in place, in part through a part-time Ph.D. program that allows employees to pursue a doctorate while at a company, so many faculty members are willing to work with students doing research in industry. Some students also gain industry experience during internships, which Marculescu sees as an integral part of engineering Ph.D. programs. Many STEM faculty members, however, frown upon internships because they usually take place during summers—a time when students can spend more time on academic research.

   Working with industry to co-advise a student will require a shift in mindset, she says. “If universities or institutions of higher education are not familiar or willing to work in that framework, it’s going to take some time to get buy-in.”

   Eric Fossum, a professor for emerging technologies at Dartmouth College who directs its long-standing Ph.D. Innovation program, also encourages students to spend summers at company internships. For Fossum, practical hands-on experience in industry is a natural part of Ph.D. training. Dartmouth is among the first group of universities who have expressed interest in joining the I-PhD program, which he describes as “a great step forward, in terms of bridging the gap between industry and academia.”

   Dartmouth’s own program includes up to six months at an industry internship and coursework to teach the business skills necessary to commercialize research. In Fossum’s experience, students love learning about the business side of technology. Historically, many faculty members have viewed applied research as having a lower standing than purely academic work, but Fossum says more academics are now also becoming interested in the practical uses of their research—something for which his work inventing the CMOS image sensor is a “poster child,” he says.

   “It’s been so rewarding to me to have that industry experience and the academic experience, research experience, all at the same time. I really believe it’s important…to integrate that process as much as we can,” Fossum says. Still, “it’s going to be a challenge for both parties to be able to speak the same language and come to a common cultural understanding about what’s expected and what might be achieved.”

   The pilot includes an external evaluation component to determine the future of the program after the initial period, and UIDP’s Boccanfuso says industry need will eventually determine its size. “Could I see thousands of students [enrolled] 10 years from now? Absolutely,” he says. “I’m biased, I think this is going to be a great addition. But let’s see what the data says.”

2. IEEE Senior Membership Demystified

   

   For most of my career, my IEEE membership sat quietly in the background—a line on my résumé, a discount code for a conference registration, and access to the IEEE Xplore digital library, which I underutilized. I didn’t think much about the grade of membership available above that of the regular member. I assumed senior membership was reserved for people further along in their career than I was. They published more papers, had more gray hair, and had worked longer in the field.

   I was wrong on all three counts. The misunderstanding cost me an important validation of my skills and professional competency.

   I suspect a lot of other qualified members are where I was one year ago: eligible but unaware of the benefits of senior membership, and one application away from a meaningful career credential.

   The myths that almost stopped me

   Here are a few of the misconceptions about senior membership:

   It’s mostly for academics and longtime IEEE volunteers.It isn’t. The grade is explicitly built around a person’s professional engineering experience. Plenty of successful applicants have never published a paper. Industry experience counts for a lot.

   You need a graduate degree.You don’t. A bachelor’s degree plus enough years of qualifying experience is sufficient on its own. An advanced degree simply offsets some of the required years of experience.

   If I’m not well-known in my field, I won’t qualify.Senior membership isn’t a popularity contest. Rather, it hinges on whether you meet specific experience metrics. The requirement is “sustained, significant technical contribution,” not “known beyond your organization.”

   I should wait until I have more significant achievements to point to.I believed this for longer than I should have. If you meet the 10-year experience threshold with five years of significant performance, you’re already eligible. Waiting doesn’t strengthen a qualifying application; it just delays getting a credential you’ve already earned.

   Why I applied for senior membership

   The push to apply came from a practical need. As a senior data scientist at Apple in Austin, Texas, I work in applied machine learning, building large-scale systems that affect customer-support operations. I already had started taking on more peer-review work—checking papers for journals including Neural Networks and IEEE Transactions on Knowledge and Data Engineering, mentoring at Apple, and writing on public platforms such as Medium and SimpleTalk.

   I wanted a credential that reflected that shift from “engineer who codes” to “engineer who helps shape the field.”

   The IEEE senior member grade turned out to be the validation of my work I was looking for. It’s not an award for a single achievement. You have to apply for it, and it’s a peer-evaluated process that confirms you’ve sustained a meaningful level of professional contributions over time.

   That distinction matters. Having a research paper published or being granted a patent proves a moment in time. Senior membership reflects a pattern of continuous contributions.

   The benefits to my career happened faster than I expected. It strengthened how search committees, IEEE conference organizers, and IEEE awards panels viewed me. Only senior members can hold certain IEEE leadership positions.

   The senior grade also opened doors to editorial and reviewer roles I hadn’t even pursued before. Journal editors and conference organizers often look for reviewers with a track record they can verify quickly, and senior membership gives them that signal without extra vetting on their end. It also gave me a credential I could point to in professional contexts, including, in my case, supporting documentation for a U.S. employment-based immigration petition, where third-party peer recognition carries real evidentiary weight.

   Navigating the process

   The process for applying for senior membership is easier than the title might suggest. To qualify, you need a combination of professional and academic experience in an IEEE-designated field: engineering, computer science, information technology, physical sciences, mathematics, or technical communications. The two must total at least 10 years, with at least five of them showing significant performance. Crucially, experience isn’t limited to job titles. Graduate research, technical leadership, and progressively responsible engineering work all count toward the total number of years. I’d been quietly accumulating qualifying years without ever framing them that way.

   “I suspect a lot of other qualified members are exactly where I was a year ago: eligible but unaware of the benefits of senior membership, and one application away from a meaningful career credential.”

   You submit your application through IEEE’s member portal, mapped against the experience requirement, along with three references from current IEEE members—at least two of whom must be senior members or IEEE Fellows who can vouch for the credibility of your work.

   The IEEE member grade evaluation committee reviews applications and renders decisions.

   How to find references

   The part everyone underestimates is references.Applications can stall at this point. References must be IEEE members in good standing, and at least two need to be IEEE senior members—which means you can’t necessarily ask people who know you best. You need to find references who are both willing to vouch for you and are grade-eligible.

   My advice is to identify and confirm all three references before you submit your application. It might be difficult to add or swap a reference during the process, and a stalled reference could delay your file.

   Where to find references is the part I worried most about. But it turned out to be far easier than I expected.

   Here are several sources:

   • IEEE Collabratec.This is IEEE’s professional networking platform and, in my opinion, is an underused resource. You can search by technical interest, geography, or society membership and message members directly. I found several of my eventual references this way—colleagues I’d never have thought to ask simply because we hadn’t worked together directly, but ones who knew my technical work through shared communities or conference circles.
   • Coworkers and colleagues, current and former.If you’ve worked alongside IEEE members—especially ones senior to you—they’re often the most natural fit because they can speak specifically to your day-to-day technical contributions.
   • Former professors.If you did graduate work, your advisor or committee members are usually IEEE members and are well positioned to speak to your research contributions, even years later.
   • LinkedIn.A surprising number of my qualifying references came from reconnecting with people on LinkedIn I’d lost touch with professionally. A short, specific, polite message explaining what you’re applying for and why you thought of the person can go a long way.

   A pattern I noticed when looking for references is that people are generally glad to be asked. Serving as a reference is a small lift for them and a meaningful one for you. Most senior engineers remember someone doing the same for them and are happy to pay it forward.

   If you’re on the fence

   If you’ve been in the field for a decade or more, doing real technical work, and IEEE membership has been sitting quietly in the background of your career the way it did in mine, it’s worth 10 minutes to check the eligibility criteria against your history. You might find, as I did, that you qualified for the membership upgrade a while ago.

3. What It Takes to Be an Adaptable Engineer

   

   The AI boom has disrupted the way engineers work, introducing new tools to learn, raising expectations for what teams can achieve in a workday, and making it harder to get hired in the first place. This makes it difficult to advise students on which specific coding languages or technical skills they should learn. So amidst the uncertainty, advice for young professionals often turns to a common refrain: Be adaptable. But what does adaptability look like in practice?

   Engineers often operate on the cutting edge of technology, so dealing with change is a normal part of the job, says Samantha Brunhaver, an associate professor of engineering at Arizona State University, in Tempe. Yet university curricula and training in the workplace often don’t prepare students for this.

   “We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it,” says Brunhaver, who received a National Science Foundation award in 2020 to study how to foster greater workplace adaptability among young engineers. For this ongoing project, she has interviewed engineering managers, early career employees, and undergraduates about their experiences.

   Part of the problem, she says, is that every employer has its own idea of what to be adaptable means. Generally, Brunhaver defines adaptability as “the ability to recognize that a change or uncertainty is occurring, and then respond effectively to that change.” But the skill is context-dependent. In software engineering, that might mean responding to turnover in the tools you use on a daily basis, while aerospace or biomedical engineers may need to keep track of changing procedures and regulations. “Managers are all saying adaptability is important,” Brunhaver says, “but defining it in different ways.”

   At the same time, engineers are all contending with changes beyond these industry-specific expectations. Jobs in the technology, media, and telecom sectors are experiencing the fastest pace of skill turnover, according to a June 2026 report on the effects of AI from the professional services network PwC. And the World Economic Forum’s most recent Future of Jobs Report, published in 2025, found that employers across all sectors expect 39 percent of workers’ core skills to change by 2030. This uncertainty can be uncomfortable. But with the right mind-set and support from leadership, adaptability can help keep you afloat.

   How to Cultivate Adaptability

   The AI transition is a big shift—but not an unprecedented one, says Jenna Butler, a research scientist at Microsoft who studies developer well-being and productivity.

   During this type of paradigm shift, there is often a “chaos period” when a new normal is being established, Butler says. In AI’s case, it challenges the understanding of what a computer can do. “I think we’re still in this in-between, difficult period that we’ve seen before, but [it] is maybe moving faster than it has historically.” Software engineers—in one of the fields most affected by AI—are now facing a significant increase in code review. “If you ask 20 developers, you get 23 different ways of working with it. Everyone is trying to sort it out,” says Butler, who describes this period as “the uncomfortable middle.”

   “We tell engineers that they need to be adaptable when they graduate, but we don’t actually explain what that means, demonstrate what that looks like, [or] help make sure that they’re developing it.”– Samantha Brunhaver, Arizona State University

   Brunhaver says one way educators can help prepare students before they enter the workforce is by offering a diversity of real-world experiences, such as internships, team-based projects, community service, and leadership roles. Each of these teach students to adapt to different challenges, easing their transition from school to work.

   It’s also important to encourage reflection, Brunhaver adds, noting that metacognition helps individuals use the skill more effectively. “In order to adapt, you have to think that you have agency and the ability to get through a situation.” Ultimately, it comes down to three steps: Perceive a need to adapt, evaluate your options, and act.

   For those already in the workforce, that action may mean taking the time to learn new tools and ways of working. Software engineering, for instance, may soon rely more on prompting models and managing agents than coding line by line. “I think people who went into software because they like solving problems are going to have a lot of fun, and people who just enjoy the art of writing code are not,” Butler says.

   The More Things Change…

   Although the tools engineers use on a daily basis are evolving, the core responsibilities of the job are more stable than they may seem, says Andy Hunt, a software developer who coauthored The Pragmatic Programmer (Addison-Wesley Professional) in 1999. The book outlines practical coding principles, and has been taught in many computer science classrooms. When Hunt was working on the 20th anniversary edition of the book, he was surprised by how much of the advice still applies. And now, seven years later, he maintains that belief.

   “The fundamental part of the job is problem solving and communication, and that’s always going to be there,” he says.

   Hunt emphasizes the importance of developing systems thinking over particular tools. To him, identifying as a Java programmer, for instance, is “like a carpenter saying, ‘I’m a hammer user,’ or ‘I specialize in cordless drills.’ ”

   He acknowledges that today’s hiring process, in which companies often filter résumés for certain languages or years of experience, makes it harder to embrace a more expansive way of relating to your job. Employers, he says, should recognize that “the tech’s not the hard part, and it never has been. Understanding information theory, understanding systems thinking, understanding what constraints you’re up to—that’s still the hard part.”

   With this type of misalignment between employers and employees, AI is also intensifying an old source of tension: How can engineers slow down enough to adapt and learn new tools when the pressure to become more productive keeps mounting?

   Who’s Responsible for Enabling Change?

   Young engineers need to embrace change. However, educators and employers also play a role in building a successful workforce. From the educator’s perspective, Brunhaver says “we need to be more explicit about what [adaptability] means and why it’s important.” Managers, meanwhile, should invest in their employees’ professional development.

   Microsoft research scientist Butler often encourages leadership to set aside intentional time for continuous learning for their engineers—even just an hour a week—without any expectation that they will produce code or progress in their daily work. “I realize that’s difficult,” says Butler. “I would encourage people to do it on their own, but I would really encourage organizations and leaders to do it, because you’re not going to get this sudden change in your people if they don’t have time and space to learn how to work differently.”

   This also means providing enough instruction, Butler adds. When developers aren’t given enough guidance on adopting something new, while being pressured to increase productivity, they risk doubling down on the tools they already know and burning out.

   “I do imagine the next number of years could be challenging,” Butler says. Engineers will have to adapt to find their place in an evolving workforce—but they also have a say in shaping that future.

   “Being adaptable sort of implies that you’re going to change based on what’s happening around you, and I would really like people to realize the change that’s happening is somewhat up to us,” she says. All individuals have a choice in how they use AI, for instance, and which models they use. “We need to be adaptable and go with the flow to a degree, but we also need to be directing that flow. The future with AI is absolutely not predetermined.”

   This article appears in the September 2026 print issue as “The Adaptable Engineer.”

4. This IEEE Senior Member Develops AI Tools for E-Commerce Sites

   

   Balaji Ingole rarely saw televisions while growing up in Udgir, India. No one in the small Maharashtra village had computers or phones. Only one household owned a television, and neighbors often gathered there to watch shows together.

   Ingole never even saw a computer growing up. It wasn’t until he reached middle school that he encountered a computer lab, an experience he says changed his life. Almost immediately, he says, the machine felt like a window into a different scale of possibility for him.

   Balaji Ingole

   Employer

   Amla Commerce in Milwaukee

   Title

   Project manager

   Member grade

   Senior member

   Alma maters

   COEP Technological University and Welingkar Institute of Management, both in India

   “I was very studious and not very social, always reading or solving problems in a math textbook,” he says. “At the computer lab, I began learning the C programming language—which was like discovering a whole new world. I was fascinated that you could create something with just a few lines of code.”

   His early interest grew into a self-directed education. Outside of Ingole’s formal classwork, he taught himself to build database-backed applications, wire up hardware, write software, and trace error logs.

   Today the IEEE senior member similarly splits his time. During the week, he’s a project manager in Milwaukee at B2B e-commerce company Amla, leading AI-driven digital transformation initiatives to help the company’s clients boost their sales. On weekends, he leads a similarly demanding life as an independent researcher. His current projects include developing AI-enabled health care diagnostic tools and assistive technologies to support people with physical disabilities.

   “I believe in ‘learn by doing,’” he says. “I really like to test my knowledge and prototype ideas to find out if they truly work.”

   A college project becomes an inspiration

   Ingole’s tendency to go beyond his coursework continued after he graduated high school in 2004. As a mechanical engineering undergraduate at The College of Engineering, Pune (now COEP Technological University), in India, he participated in several extracurricular activities. One was interviewing entrepreneurs and writing about them for The COEP College Magazine. The experience helped him gain confidence, he says, giving him the push he needed to pursue interviews for the publication with two Indian entrepreneurs he admired: N.R. Narayana Murthy, cofounder of IT giant Infosys; and his wife, philanthropist Sudha Murty. The Murtys cofounded the Infosys Foundation, a nonprofit that runs educational, health care, women’s empowerment, and sustainability programs in underserved areas of India.

   “Every week I would fax them: ‘Please give me an interview time,’” Ingole says. Eventually, Sudha Murty’s office offered him a phone interview, but he requested to meet her in person at Infosys’s Bengaluru offices. She agreed, but the offices were 940 kilometers from Pune, and he didn’t have the money to travel or stay overnight in a hotel.

   Ingole and a classmate borrowed money from friends and traveled through the night on multiple buses and trains to get to Bengaluru. They freshened up in a public bathroom before heading to the Infosys campus to meet Murty.

   Impressed by their persistence, she surprised them by also arranging a brief chat with Narayana Murthy.

   “Narayana Murthy handwrote a personal message to the engineering students of [my college]—which we proudly published in our college magazine,” Ingole says. “In his note, Murthy shared that we are at an extraordinary moment in India’s history and that the future looks even brighter. His words encouraged us to work hard and make the most of this time.

   “I still have that note,” Ingole says. “They are billionaires, and I was just a regular student. The fact that they took the time to do this really motivated me.”

   During the final semester of his engineering studies, Ingole joined the Tata Research Design and Development Center in Pune for a six-month internship. After earning his bachelor’s degree in mechanical engineering in 2008, he became a graduate engineering trainee at Honeywell Automation in Pune.

   He left the company in 2009, and during the next 13 years, he held different software engineering and project management positions at IT companies across India.

   He earned a master’s degree in business administration from the Welingkar Institute of Management, Mumbai, in 2017.

   In 2022 he accepted a project-manager role at Mars IT Solutions in Madison, Wisc. The following year, he left to join Gainwell Technologies, also in Madison, as a senior project manager. At Gainwell, he managed projects for the core IT systems multiple U.S. state health departments use to administer Medicaid benefits, manage provider enrollment, and verify member eligibility. The experience managing projects that directly enabled patients’ access to health care gave Ingole a special appreciation for and interest in this area, he says.

   “Health care data is not like other data,” he says. “The stakes are high, compliance requirements are different, and the margin for error is effectively zero.”

   Ingole says he enjoyed the rigor of data governance combined with the potential to positively impact lives, and that also applies to his current work at Amla.

   Agentic AI in e-commerce

   Ingole joined Amla in July 2025. He helps manufacturers and B2B customers modernize their e-commerce operations. He also builds AI tools for them and for his internal team.

   For Amla’s customers, he’s developing AI-enabled chatbots that help manufacturers set up and manage large product catalogs in e‑commerce platforms. Such product setup traditionally has been a manual, tedious, error-prone process: Companies upload thousands of products, adjust item names, enter prices, update images, and more.

   “Product setup has been one of the most painful processes in e-commerce, and it can take [our] customers two to three months to complete,” Ingole says. “We’re creating an AI agent that will guide them, step-by-step, to get everything set up in two weeks.”

   Ingole relies on AI agents for some of his own tasks at Amla. Project managers historically have spent 10 to 12 hours each week assembling and sending status reports to stakeholders. Ingole built an AI agent to handle much of the work.

   “It runs every Monday morning and reads through my emails to extract highlights, risks, timelines, and upcoming releases, then sends me a written status report,” Ingole says. The process might sound simple, but the agent’s workflow involves at least a dozen steps including defining parameters, managing temporary files, and integrating with existing tools.

   With the information-gathering work handled, it frees up Ingole and his colleagues to spend more time on deeper-thinking work, he says.

   Publishing as idea refinery

   For nearly a decade, Ingole has spent some of his free time conducting independent research projects in data analytics and AI-enabled applications in health care. He has written more than 40 peer-reviewed papers, which are in the IEEE Xplore Digital Library. He has been granted six patents in the United Kingdom and India. In the U.K., he is a registered coinventor of an AI-powered, cloud-connected wearable device for health monitoring and an AI-based breast cancer detection tool.

   Ingole’s patent for the breast cancer detector, he says, reflects his belief that when engineers apply data and AI correctly, they can help doctors diagnose patients more quickly and accurately.

   That, he says, is both a power and a responsibility.

   He is part of a team helping patients who are paralyzed and nonverbal control items in their environment. His goal, he says, is to develop a brain-computer interface to let patients turn on a fan, switch off a television, and complete similar tasks.

   Publishing research requires both academic rigor and peer scrutiny, and Ingole says the function has been critical to improving as both a project manager and a researcher-inventor.

   “Lots of research ideas never make it to paper,” he notes. “But when you write for journals or conferences, you’re bombarded with questions from Ph.D.s and experienced researchers. This forces me to refine my methodology, and to combine use cases and technical architecture in a way that stands up to expert review.”

   Finding a professional hub

   Ingole joined IEEE in 2022, and he says the affiliation has become central to both his research and his professional identity.

   “I use the Member Directory often and contact engineers through my IEEE email address, which gives me credibility because they know it’s a genuine research connection,” he says.

   The organization has given him a platform to contribute to the research space beyond his own papers, he says. He has served as a conference session chair, keynote speaker, technical program committee member, and peer research reviewer for various conferences and events. His IEEE membership, he says, has opened doors to other communities, helping support his entry into the British Computer Society, which has stringent acceptance criteria.

   Those opportunities have helped him build a global network of collaborators with whom to discuss upcoming research, seek advice, and share data, he says.

   “IEEE is important for me to continue as an independent researcher,” he says. “It lets me contribute to the community, and I get a lot in return.”

5. Gaining Leadership Backing for Your Innovations

   

   This article is part of our exclusive career advice series in partnership with the IEEE Technology and Engineering Management Society.

   Imagine this: You have a strong idea for a new product for your company. Your coworkers encourage you to move forward because they believe it could be the organization’s next big success. The idea clearly falls outside your department’s responsibilities, however, and you have no role in the product line.

   What should you do? Sit and wait for “the right group” to pick it up, or push the idea forward without knowing how or what it might mean for your current position?

   Such situations occur frequently. Many end up as missed opportunities, even though they could have significantly advanced the company’s technological or market position.

   Some organizations actively support such initiatives, allocating specific periods during the workday for employees to focus on developing their own ideas.

   Companies known for that include Google and 3M. They allow employees to pursue projects with a portion of their time, such as one day per week. Research that I conducted indicates it pays off for employee performance.

   Bootlegging and skunkworks

   At some companies, managers know such projects exist, but they deliberately turn a blind eye, allowing them to continue.

   Some employees persist through bootlegging or skunkworks projects.

   Bootlegging projects have not been approved by a manager or funded by the company.

   Skunkworks projects involve a small team within the company that has been given authority and funding to secretly research and develop potentially groundbreaking innovations during their off-hours. The term comes from Lockheed’s Skunk Works division, set up in 1943 in a rented circus tent to build the P-80 fighter jet in secret. It took just 143 days.

   The 3M Post-it Note came out of the company’s “15 percent culture,” described as a permitted bootlegging policy. It gives employees paid time off to pursue their own ideas.

   The company traces the philosophy to its longtime president and later chairman William L. McKnight. Company scientist Arthur Fry used the policy in 1974 to turn a colleague’s dormant adhesive into the first Post-it prototypes, after his own bookmarks kept falling out of his hymnal.

   There are several examples of high-visibility skunkworks projects. At Apple, Steve Jobs pulled roughly 20 people—pirates, as he called them—out of the company to build the original Macintosh computer in a building nicknamed Texaco Towers. In Walter Isaacson’s biography Steve Jobs, he frames the idea as modeled on the skunkworks approach.

   Google’s Gmail system is frequently—and incorrectly—cited as a product of the company’s “20% time” policy. In a 2014 interview with Time magazine, the system’s creator, Paul Buchheit, said Gmail was in fact an official assignment. What the Gmail incubation did share with classic skunkworks projects was secrecy: For much of its three years in development, it was kept hidden from most people inside the company.

   If you want to drive change in your organization, build a promoter triad around your idea.

   At Alphabet, Google X—now known simply as X—operated as a secretive “moonshot” lab, kept hidden from most Google employees, according to a 2011 article in The New York Times. Google’s self-driving car project graduated from X to become Waymo, and Google Glass was likewise incubated there. The X team is now developing the second edition of Glass Enterprise, a successor aimed at industrial rather than consumer use.

   Amazon runs a comparable model through Lab126, which, according to an article in Fast Company, evolved from a small skunkworks Amazon subsidiary into a hardware maker with nearly 3,000 employees. Lab126 delivered the Kindle in 2007 and the Echo in 2015.

   Then there are so-called submarine projects, which employees work on without permission and despite explicit disapproval. They can lead to disciplinary action and termination.

   Innovation management

   Innovation management theory offers a more structured and robust approach. It argues that successful organizational change requires support at several levels, according to “Teamwork for Innovation: The ‘Troika’ of Promoters,” published in R&D Management. The promoter theory, developed around 25 years ago, consistently shows that change projects are far more likely to succeed when they are supported on multiple organizational levels. A good idea alone is not enough; you need a network of technology, process, and power promoters to turn a concept into a fully implemented, scalable solution.

   First, you need a technology promoter: the person who has the idea, such as a new product, and possesses technical expertise and specific knowledge about the field or industry. Art Fry at 3M would be such an individual.

   How can you put that into practice as an individual? Start by clearly formulating your idea into a concise concept paper or one-page summary including benefits, technical feasibility, and potential business impact.

   Identify potential technology promoters (experts who can validate and refine your idea), and approach them early to strengthen the technical foundation.

   In parallel, map the relevant stakeholders and decision-makers, and identify process promoters who understand how decisions are made in your company. They could be colleagues in innovation, R&D, or business development who understand your idea and how it can benefit the company.

   The second is a process promoter: someone who might not know all the technical details but understands the organization’s formal and informal networks and knows how to navigate its processes, committees, and decision-making paths. This person can ensure the idea reaches the right stakeholders at the right time.

   In the 3M case, it would be a person from the organizational management department, often called an innovation manager. The key role here is to connect inventors such as Fry with people from other departments needed for further project development, such as manufacturing, quality control, and sales.

   Lastly, there’s the power promoter: a person in a leadership position who might not know the technical details but can allocate resources, eliminate obstacles, and maneuver through the company’s political dynamics. This individual has hierarchical power and acts as a sponsor of the idea or project. In the case of Fry, the person could be, say, the chief technology officer, but it also could be a middle manager who has the power for an individual field of action.

   The three-level promoter structure applies regardless of whether the change concerns a new product, new service, or internal process innovation.

   Engage potential power promoters by presenting a low-risk, small-scale pilot and a clear value proposition. Leaders are more likely to support ideas that are well prepared, vetted for potential risks, and backed by a small coalition.

   Building the promoter triad

   In short, don’t work in isolation. Systematically build alliances across expertise, networks, and hierarchical levels to create lasting change. If you want to drive change in your organization, build a promoter triad around your idea.

   The tech experts and leadership promoters are easier to identify. Process promoters are often found in corporate innovation management, R&D management, or strategy functions, but they also can emerge in line units with strong internal networks.

   Innovation management, as the promoter model describes it, looks nothing like the management structure most engineers are trained to expect. Traditional technical management runs on a single reporting line. With the promoter model, influence is spread across three people—technology, process, and power promoters—who may be in different departments, at different levels of seniority, and who might never share a reporting line.

   What holds the trio together isn’t a formal structure; it’s the idea itself, for as long as it takes to move the idea forward.

   That makes innovation management closer to networked, matrix-style leadership than to the pyramid most engineers picture when they hear the word management. It’s worth understanding both models before you decide which kind of impact you’re actually optimizing for.

   The Institute has covered the tension from the individual’s side in “Tips for How to Think Like an Entrepreneur,” “Management Versus Technical Track,” both published in partnership with the IEEE Technology and Engineering Management Society, and “What to Consider Before You Accept a Management Role” from the IEEE Spectrum Career Alert newsletter. All are worth a look if you’re weighing a formal management track against staying close to the technology itself.

   Remember: You don’t have to build your promoter network alone or only inside your own company. IEEE societies, sections and chapters, and technical committees, as well as the networking platform IEEE Collabratec, function as a ready-made cross-company network. They are practical places to find technology promoters with deep expertise in a field you don’t fully own yet, or to meet process and power promoters at other organizations who have built a promoter coalition around a similar idea.

   For more tips on how to advance your career, check out our Career Advice for Engineers, From Engineers collection.

6. IEEE Presidents’ Scholarship Honors Teen Innovators

   

   About 16 percent of the global population—more than 1 billion people—live with some form of disability, according to the World Health Organization. Many of the disabilities affect independence and mobility.

   Three high school students working on inventions to help people with disabilities restore movement, translate thoughts, and navigate rough terrain had their work showcased at Regeneron’s International Science and Engineering Fair (ISEF), held in May in Phoenix. Their projects earned them this year’s IEEE Presidents’ Scholarship awards.

   IEEE President Mary Ellen Randall presented the awards at a ceremony held during the fair. They also received an IEEE President’s coin, which students said was a highlight of their experience.

   Hollie Tang won this year’s IEEE Presidents’ Scholarship of US $10,000 for her wheelchair navigation system. The award is payable over four years of undergraduate university study and includes a complimentary IEEE student membership.

   Partap Sidhu, the second-place winner, received a $600 scholarship for his mind-controlled lower-limb exoskeleton. Third-place winner Calvin Shang Hung received a $400 scholarship for his rough-terrain robot. Sidhu and Hung also got complimentary IEEE student memberships.

   Established by the IEEE Foundation and administered by IEEE Educational Activities, the Presidents’ Scholarship recognizes high school students who demonstrate an exceptional grasp of electrical engineering, computer science, or another IEEE field of interest.

   Controlling movements with a tongue

   Holly Tang won the 2026 IEEE Presidents’ Scholarship of US $10,000 for her Tonguage project, which is a noninvasive, computer-vision-based human-machine interface.Lynn Bowlby

   Tang, a sophomore at Wilson High School in Hacienda Heights, Calif., secured the top prize for her Tonguage project: a noninvasive, computer-vision-based human-machine interface. Using tongue movements and a standard camera, the interface lets users control a computer and other digital tools as well as assistive technologies including wheelchairs. The tongue pad, one of the system’s core features, allows the user’s tongue to function as a directional cursor, while eye blinks serve as mouse clicks.

   Tonguage translates the person’s tongue and eye motions into actionable commands in several ways, such as the tongue’s position inside the mouth and continuous movement patterns. The system’s multimodality combines input from the tongue with other facial cues.

   The system includes a face-tracking feature for error prevention that verifies commands are coming from the intended user, disregarding anyone else who moves into the camera’s frame.

   That is a critical safety measure for a wheelchair-navigation application, Tang says.

   Accessibility was central to Tang’s mission. She built the system to run on relatively affordable, readily available laptop cameras rather than more costly specialized hardware.

   “Mobility conditions don’t discriminate,” she says. “They can affect anyone of any income, gender, and socioeconomic status.”

   Tang initially imagined Tonguage as a simple substitute for a keyboard and mouse. The more research she did, though, the more she realized that it could offer autonomy through applications such as wheelchair navigation, robotic arm control, and gaming, she says.

   “We’re so focused on trying to give people autonomy over just basic human tasks that we often leave out things like gaming,” she says. “They deserve the freedom to play games and enjoy entertainment as well.”

   Tang, who plans to pursue biomedical engineering, says a visit to a rehabilitation center solidified her purpose.

   “Including empathy in your technological solution is so important,” she says. “Empathy is hard to teach in a classroom, but it can be learned through experience, and through actually meeting people whose lives your work might change.”

   Mind-controlled exoskeleton

   Sidhu, a junior at Bethpage High School, in New York, took second place for NeuroGait, a mind-controlled, lower-limb exoskeleton. He says he was inspired by his volunteer work at a community center that lacked elevators. He saw individuals with mobility issues struggle to navigate the three flights of stairs.

   NeuroGaitoperates by reading the Bereitschaftspotential (BP), a faint electrical pattern that emerges one to two seconds before a person consciously initiates movement. Using a custom electroencephalogram (EEG) headset and a convolutional neural network (CNN), the system classifies intended movements and sends commands to a 3D-printed exoskeleton. Rather than rigid motors, the suit relies on pneumatic artificial muscles that Sidhu designed to mimic human anatomy.

   “The pneumatic artificial muscle in itself is so compliant that it’s able to adjust to the limitations of the human body,” he says.

   The technical specifications are striking: The CNN achieves a 99.9 percent accuracy in detecting a person’s intended movement, while the full system—from the brain’s signal to physical movement—operates at 95.2 percent accuracy, according to the results from 500 trials Sidhu conducted.

   Perhaps most impressively, Sidhu built the entire system for about $276, less than 1 percent of the $40,000 to $100,000 price tag of commercial exoskeletons, according to a 2025 revenue report from Roots Analysis.

   He says he hopes to bring NeuroGaitto the community center where the idea for the project began.

   He attributes his success to staying current with research from institutions and organizations such as Boston Dynamics and MIT.

   “To be successful in research,” he says, “you have to know what’s being done right now.”

   A spider-inspired robot

   Hung, a sophomore at El Cerrito High School, in California, took third place for Math Into Motion: Robotic Hexapod for Hazardous Environments. The six-legged robot is designed to traverse terrain too unstable for humans or conventional robotic systems.

   With only weeks before the science fair deadline for entries and no prior electrical engineering experience, Hung began with an idea inspired by his interest in spaceflight: an insectlike robot. He had spent years watching rovers such asCuriosity and Perseverancestruggle on uneven surfaces, leading him to hypothesize that a hexapod design would be better for rugged ground.

   As the project progressed, the humanitarian applications for his robot became clearer, he says. Watching news reports of the earthquake that struck Türkiye in 2023, as well as conflicts around the globe, Hung adapted his robot for use in disasters. The hexapod’s stable tripod walking gait, in which three legs stay grounded while the other three move, makes it well suited for navigating in collapsed buildings to locate survivors or to carry sensitive supplies such as insulin in conflict zones.

   The current version moves using three mathematical techniques. Inverse kinematics converts a target leg position into the motor angles needed to reach it. Linear interpolation breaks each movement into a series of smaller steps for smoother motion. And Euclidean transformations translate the robot’s travel direction into instructions that each leg can follow, regardless of the way a leg happens to be facing.

   Hung taught himself how to design a printed circuit board. He also taught himself 3D modeling, coding, and soldering. Figuring out the complicated mathematical transformations to coordinate legs facing different directions proved to be the toughest hurdle, he says.

   After seven months of development and trial and error, a critical circuit board failure in his third version nearly ended the project, he says.

   “There was a really strong moment of ‘Should I just give up?’” he recalls.

   He simplified the design and rebuilt it from the ground up.

   “I just decided to double down,” he says. The fourth version of the robot was the first that successfully walked across his living room floor.

   He advises aspiring engineers that “if you find the right project and it truly becomes your passion, designing it almost starts to feel like fun, and that’s what carries you through.”

   As the three young innovators demonstrate, the future of engineering goes far beyond technical ingenuity. Much is rooted in empathy and a commitment to human welfare.

   Through initiatives such as the IEEE Presidents’ Scholarship, the IEEE Foundation showcases and nurtures bright minds poised to shape the next era of assistive technology and robotics.

   For Tang, Sidhu, and Hung, the ISEF stage is just the beginning. They can look forward to impactful careers dedicated to advancing technology for the benefit of humanity.

7. Digital Signal Processing Pioneer Bede Liu Dies At 91

   

   Bede Liu, a digital signal processing pioneer, died on 7 May. He was 91.

   Liu was widely regarded as one of the founders of modern digital signal processing, a field that applies mathematical algorithms to analyze, modify, and transmit signals including sound, images, and video.

   The IEEE Life Fellow taught electrical engineering at Princeton for more than 50 years. From 1994 to 1997, he chaired the university’s electrical and computer engineering department.

   Liu’s research aided the transition from analog to digital processing of sound, images, and video. His work helped establish many of the mathematical and engineering techniques that underpin modern communications, multimedia systems, and consumer electronics.

   Although little known outside engineering circles, his work is embedded in technologies used by billions of people. The low-power digital signal processors that make cellphone calls, streaming video, and Internet communications possible can be traced to research he conducted in the 1970s and ‘80s.

   Liu received the 2018 IEEE Jack S. Kilby Signal Processing Medal for “sustained contributions to the analysis and the development of low-complexity realizations of digital signal processing algorithms.”

   “We stream music and video. We take photos with our phones, and we send them around. We don’t even think about it,” IEEE Life Fellow H. Vincent Poor said in an obituary for Liu. “But it’s all because of the signal processing, image processing, and video processing that’s been developed over the years, as well as other technologies that have grown up beside it and enabled it, like semiconductors. The development of these processing advances was exactly what Bede was a major part of.” Poor is a professor of electrical and computer engineering at Princeton.

   An impactful scholar and teacher

   Liu was born in Shanghai in 1934. During his childhood, his family relocated to Taiwan amid the upheaval of the Chinese Civil War. His father, Henry Liu Sr., was an electrical engineer.

   Liu earned his bachelor’s degree in electrical engineering in 1954 from the National Taiwan University, in Taipei. After graduating, he and his family moved to the United States. Liu and his father attended the Polytechnic Institute of Brooklyn (now the New York University Tandon School of Engineering) together. They earned their master’s degrees in electrical engineering in 1956. Liu continued his studies at the school, earning a doctoral degree in electrical engineering four years later.

   In 1959 he was awarded a Bell Labs fellowship and worked at the company’s Murray Hill, N.J., location until he joined Princeton in 1962.

   “Liu was a highly impactful scholar and teacher—always thinking ahead of future needs and changing technologies,” said IEEE Life Fellow Peter J. Ramadge, a Princeton professor emeritus of engineering.

   Cellphones make use of a considerable amount of digital signal processing, Liu once noted. Many of the field’s advances, he added, involved making sophisticated processing practical on devices with limited computing power—which is the challenge that confronted generations of engineers designing portable electronics.

   Liu’s research contributions helped shape both the theory and practice of digital signal processing. With Abe Peled, a former graduate student, he authored the 1976 textbook Digital Signal Processing: Theory, Design, and Implementation, which is a standard reference for engineers. Published before digital signal processing had fully emerged as a distinct discipline, it helped define the subject for practitioners and students around the world.

   Liu also published 250 technical papers and was granted 12 U.S. patents. His papers are available to read on the IEEE Xplore Digital Library.

   The first patent granted to him and Peled was in 1976 for a hardware design that processed bits in parallel, rather than in sequence. The innovation greatly increased computing efficiency for data including sound and communication signals.

   Peled says Liu “demonstrated an openness to new ideas and a willingness to challenge the orthodoxy of the EE department at that time—which leaned heavily toward more theoretical information theory.”

   A mentor to well-known engineers

   Liu’s influence extended beyond his own research. He advised 53 doctoral students, many of whom went on to distinguished careers in academia and industry, including leadership positions at Google and IBM. One former student, computer scientist Robert Kahn, helped create the architecture of the modern Internet. Kahn, an IEEE Life Fellow, received the 2024 IEEE Medal of Honor.

   “His former students were very successful,” Poor said of Liu, “and I think that’s a testament to his skill as a mentor.”

   “Liu was a highly impactful scholar and teacher—always thinking ahead of future needs and changing technologies.”—Peter J. Ramadge

   Together with several Ph.D. students, Liu developed methods of filtering and compressing digital signals to mitigate errors and dramatically reduce the computation needed for signal processing.

   As digital signal processing moved from laboratories into commercial products, the impact of Liu’s ideas spread across industries. His research helped spawn the development of lower-cost and lower-power electronics and contributed to advances in mobile communications, multimedia technology, industrial automation, and biomedical imaging.

   A focus on media integrity and copyrights

   In the 2000s, Liu turned his attention to media integrity and copyright issues.

   “With the increasing accessibility of digital media source material, the protection of ownership and the prevention of unauthorized alteration has become an important concern,” he wrote in his 2002 book, Multimedia Data Hiding. The book, which he co-wrote with his former doctoral student IEEE Fellow Min Wu, discussed the theory, techniques, applications, and security of digital watermarking—hidden signals that could identify a genuine copy of a song, image or video to prevent unauthorized distribution or tampering.

   A Princeton team that included Liu, Wu, and another of his doctoral students uncovered serious vulnerabilities in watermarking technologies being considered by an industry consortium. They found that the standardization efforts were immature and would not protect against digital piracy.

   “Now nearly every copy of a Hollywood film given to a critic or theater carries a unique digital forensic watermark to prevent unauthorized redistribution,” said Wu.

   A force in the community

   Liu, an active IEEE volunteer, served on the IEEE Board of Directors in 1984 and 1985. He was the 1982 president of the IEEE Circuits and Systems Society.

   He was a member of the U.S. National Academy of Engineering, an academician of China’s Academia Sinica, and a foreign member of the Chinese Academy of Sciences.

   Outside the classroom, he was recognized for his humility, humor, enthusiasm, and generosity. When thinking of Liu, IEEE Life Fellow Kenneth Steiglitz says, cheer is the first word that comes to mind.

   Liu was “always ready with a positive remark, a quick smile or, maybe, some tips on the right way to cook a duck,” says Steiglitz, professor emeritus of computer science at Princeton.

   Liu encouraged his students to take on ambitious, unconventional projects, and he inspired students and colleagues with his adventurous spirit.

8. Bring a Product Manager Mindset to Your Next Engineering Job

   

   If you haven’t already seen a job listing for a “product engineer,” you probably will soon. The job everyone’s suddenly hiring for, this role is like a cross between a product manager and an engineer (as the name suggests). And it’s a hiring trend worth paying attention to.

   Companies are opening more of these roles every single month, but they’re struggling to fill them. The reason has almost nothing to do with engineers’ coding skills or years of experience.

   The best career move you can make to prepare for these types of roles has almost nothing to do with getting more technical. Instead, it comes down to one of the fluffiest, most overused, and potentially cringiest words in all of tech: mindset.

   Stick with me, I promise this goes somewhere useful.

   The problem: We were trained to be task-takers

   When I started out, my job looked like this:

   Drive to an office. Sit through meetings that led to other meetings until a project manager handed me a task they’d already chopped into tiny pieces.

   My job was to turn that task into code.

   It took years for me to get good at a coding language and tech stack, and once I did, I executed that knowledge against specs that somebody else wrote.

   You know what’s freakishly good at that exact job? I’ll give you a hint: It starts with A and ends with I.

   Boris Cherny, the creator of Claude Code, recently said: “coding is basically solved,” and “the bottleneck is going to be good ideas.”

   So if your entire value is “hand me a task and I’ll build it,” you’re in a footrace with the robots. I don’t like that for you.

   The bad news... that is also good news

   Many companies are flattening. Middle management is getting stripped out, for better or worse (mostly for worse), which means many of us are doing more with less.

   This might sound like purely more work, but it’s also an opening for anyone who cares about what they’re building and can put on their manager hat. Companies are no longer just hunting for the strongest engineer in one narrow domain.

   What’s rare, and what actually moves revenue, is an engineer who can spot the thing that’s quietly costing money and either flag it to leadership or just go fix it.

   What this actually looks like

   Being product-minded has NOTHING to do with your tech stack.

   Here’s where to start:

   Have an opinion and back it up. As a former engineering manager, the worst thing I ever heard was silence. I’d often ask the team what they thought because I doubted myself and wanted a gut check. I was grateful to the ones who said “nope, bad idea, here’s why.” Pushback is a gift.

   Learn the domain, casually. Work for a plumbing company? You don’t need to become a plumber, but spend an hour on Reddit threads where plumbers vent. Now your ideas come from your potential customers.

   Make experiments cheap and safe. This is where any engineer has massive leverage. Experiments are not free. A bad one loses customers and frustrates users. Tools like LaunchDarkly and Optimizely let you ship a change to 5 percent of users and roll it back the second it tanks. Learn them, or build a scrappy version yourself. A team that can quickly run safe experiments will out-learn everyone else in the building.

   Be data-driven. Stop fighting about button colors. Pick a goal: making money, finding product-market fit, or making the product sticky so people come back. Then measure it. If your gorgeous redesign tanks time-on-site, it failed, no matter how good it looked to you. If the ugly version makes more money, ship the ugly version.

   You don’t have to be the ideas person. Maybe you’re not a visionary. That’s fine. Organize a hackathon around an actual company goal. Pull up your company’s quarterly targets and build something against one of them. Don’t know what those targets are? That’s your first assignment.

   Good ideas are the new bottleneck—and they always have been

   When I was a manager, I asked myself one question every week: What’s the single most impactful thing I could do right now? The answer was almost never “write more code.” It was understanding a gnarly problem nobody had defined yet. Building a deck to spread knowledge that was in one person’s head. Getting the right three people in a room to actually make a decision we’d been putting off.

   Code is cheap, and it always has been. We just couldn’t see it, because for decades the typing took so long that it felt like the hard part. It never was. The hard part was always knowing what’s worth building.

   — Brian

   Siobahn Day Grady Wants Everyone to Be AI Literate

   In January 2025, Siobahn Day Grady launched the first AI research institute at a historically Black college or university. The institute aims to help expand AI skills for all students at North Carolina Central University, where Grady is an associate professor, through both AI research opportunities and skills training. Though the institute is the first of its kind, Grady hopes it could serve as a model for other HBCUs.

   Read more here.

   Should Researchers Write Papers for AI Instead of People?

   AI is increasingly used in the scientific research process. So does publishing need to change to keep up? Jiachen Liu recently co-authored a paper published on ArXiv arguing that the PDF should be replaced with an “Agent-Native Research Artifact” designed with AI in mind. In this interview with IEEE Spectrum, Liu lays out a provocative vision of AI-driven research and an infrastructure that captures—and learns from—details that often get left out of today’s papers.

   Read more here.

   Detect Dark Matter’s Mark From Your Backyard

   Astronomers still don’t know exactly what dark matter is, but they can detect it—and so can you. With a small radio telescope and a few other pieces, you can create a DIY setup to gauge how fast hydrogen clouds are moving across the Milky Way. Feed those measurements into a spreadsheet, and you can see the same signals that have baffled the astronomical community for decades.

   Read more here.

9. IEEE Engineering Summit Supports Bhutan’s Digital Transformation

   

   In collaboration with the Kingdom of Bhutan government, IEEE recently introduced its Engineering Education, Research, and Innovation Summit.

   Held on 9 and 10 June in Paro, in the eastern Himalayas, the event was designed to help Bhutan navigate its digital transformation by focusing on the critical intersection of digital transformation, engineering education, and sustainable development.

   The summit brought together global academic leaders, technology experts, and Bhutanese government officials to discuss how modern engineering curricula can evolve from theory-centric models into application- and skills-based frameworks. Discussions focused on how to build high-value research capabilities in the country, integrate artificial intelligence into higher education, and address foundational infrastructure challenges to ensure equitable, nationwide digital readiness.

   “IEEE is proud to collaborate as a catalyst for progress in higher education as AI shifts the technology landscape and Bhutan prepares for its next era of innovation and resilience,” Mary Ellen Randall, 2026 IEEE president and CEO, said at the event. “Our goal is to support local universities and students as they develop trusted, future-ready technology that honors the nation’s commitment to sustainability and human well-being.”

   The event featured an address by Bhutanese Princess Chimi Yangzom Wangchuck, who emphasized the importance of aligning technological innovation with the nation’s philosophy of gross national happiness (GNH), which prioritizes well-being, sustainability, and ethics.

   “The question before us is not whether technology will shape the future; it certainly will,” the princess said. “The more pressing question is whether we can shape technology according to our values.”

   A blueprint for Bhutan’s future

   The summit helped establish a collaborative blueprint for a high-value knowledge economy in Bhutan through several key focus areas:

   • Workforce readiness: designing industry-driven curriculum modernization and cocreating skills programs to equip graduates with practical, technical competencies.
   • AI and research infrastructure: strengthening open science, trusted regional datasets, and global citation impact to prepare universities for AI-enabled learning environments.
   • Values-driven innovation: merging GNH principles with technological advancement and helping ensure new engineering practices support climate-resilient infrastructure and green innovation.
   • Institutional connectivity: using digital transformation to bridge technical capability gaps between urban and rural institutions; linking classrooms to a global research network.
   • Promoting sustainability: convening stakeholders to exchange ideas on green innovation, climate-resilient infrastructure, and engineering education.

   Expanding digital access

   To help promote the effort, IEEE offered Bhutanese universities, government institutions, and industries a six-month complimentary trial of two key technical resources:

   • IEEE Electronic Library.Delivered via the IEEEXplore Digital Library, the IEL gives users access to more than 7 million documents—including trusted IEEE journals, conference proceedings, standards, and technical papers—to enhance research, teaching, and technology development.
   • IEEE eLearning Library.This platform offers online courses developed by experts in engineering, computing, and technology, supporting flexible learning across core and emerging technical fields for professionals, faculty and students.

10. Navigating the Pivot From Tech Expert to Organizational Leader

    

    The transition from a purely technical expert or individual contributor position to a broader leadership role is one of the most challenging phases in a STEM career. It requires moving away from relying solely on technical excellence toward mastering systems thinking, adaptive leadership, and team alignment.

    To help mid-career professionals navigate the shift, the inaugural IEEE International Leadership Conference is designed to provide attendees with practical tools to step into broader responsibility and champion an entrepreneurial mindset.

    The ILC event is scheduled for 3 and 4 October in Budapest. Registration is open.

    Thinking beyond technical contributions

    To successfully step into a leadership role, technical professionals need to look beyond their individual output and focus on “understanding the larger system, and championing innovation by building trust and aligning new ideas with organizational goals,” says IEEE Life Senior Member Daniel Sniezek, cochair of the ILC program committee.

    Because engineering decisions don’t exist in a vacuum, navigating the larger system requires recognizing how technical choices intersect with the organization’s broader business, operational, and ethical realities, Sniezek says.

    By letting go of the need to be the sole technical expert and focusing instead on collaborative empowerment, he says, engineers can pivot into transformational leaders who align new initiatives with the organization’s strategic vision.

    Ultimately, over the span of a career, an individual’s leadership journey evolves far beyond personal advancement to “creating a lasting legacy through the people you develop, the knowledge you share, and the innovations you inspire,” he says.

    Solving the intrapreneur’s dilemma

    Championing disruptive ideas within established corporate structures—sometimes called the intrapreneur’s dilemma—does not mean working against the organization. Rather, it requires emerging leaders to act like business owners instead of passive task-takers.

    To begin thinking like an entrepreneur from within, professionals should shift their focus from merely executing assigned work to proactively identifying hidden areas that would create value for the company, building trust with colleagues, and presenting bold innovations as solutions to the organization’s long-term strategic goals.

    That kind of self-starting entrepreneurial mindset is how leaders create opportunities out of institutional constraints.

    IEEE Senior Member Deyasini Majumdar, cochair of the ILC program committee, advises professionals to exercise leadership and strategic thinking skills without being asked.

    “Within the constraints of established organizational structures you can unearth a treasure trove of opportunities to innovate,” Majumdar says. “Remember: A key trait of an effective leader is to engineer solutions and lead, even in the face of difficulties.”

    A multidirectional exchange

    Leadership is a multidirectional exchange of ideas across generations—which is one focus of the ILC.

    Although emerging leaders can gain invaluable strategic guidance from seasoned executives, the relationship is a dynamic, two-way street.

    Modern leadership requires established executives to remain active learners. Addressing what senior leaders can glean from their mid-career counterparts, Majumdar emphasizes, the leaders must maintain “the openness to seek opportunities, to quickly adapt, and to learn and grow with everyone around them.”

    A continuous-learning mindset is what keeps leaders agile and effective in a rapidly changing technological landscape, she says.

    The mutual openness can serve as a bridge between generations.

    Whether an emerging professional is making a mid-career pivot from technical expert to manager, or a senior executive is transitioning into a mentoring and advisory role, the fundamental rule of transformational leadership is similar. Success means shifting your focus from individual achievement to enabling the capability, growth, and legacy of others.

    Building influence and impact

    To help with the shift toward transformational leadership, the ILC is featuring sessions focused on questions professionals must ask at key career inflection points.

    Rather than a single workshop, the distributed sessions aim to address diverse professional transitions, such as evaluating promotions, learning how to influence laterally, pitching innovative projects, and sustaining leadership energy.

    Inflection points include evaluating new internal roles; building lateral or upward trust; pitching an idea about a disruptive project; and facing rapidly expanding responsibilities.

    The questions include:

    • How do I evaluate career transitions without discarding hard-won experience?
    • How can I exercise leadership through credibility and collaboration, regardless of formal authority?
    • How do I use an entrepreneurial mindset to create value and gain sponsorship for new ideas?
    • How can I avoid the early warning signs of burnout while taking on more responsibility?

    The conference is designed to equip attendees with systems thinking and communication mastery needed to cultivate influence.

    Leadership is not just about reaching the top; it is also about engaging in a collaborative effort to multiply your impact across the ecosystem.

11. IEEE Course Teaches How to Use AI to Modernize Power Grids

    

    Today’s U.S. electrical grid, among the largest, most complex systems ever built, is operating at its limit. The combination of rapid industrial growth, more frequent extreme weather, and a record surge in electricity use has pushed the grid to its breaking point, according to the U.S. Department of Energy.

    Built decades ago for a more predictable world in which power came mostly from centralized coal or gas plants and electricity use grew at a steady pace, the grid faces unanticipated strain due in part to growing demand from data centers. The jobs of professionals managing the infrastructure have evolved from traditional engineering tasks to complex, fast-moving challenges.

    Industry reports show that millions of modern digital sensors, smart meters, and grid monitors are generating nonstop waves of information. The sheer volume of data requires instant, automated computer analysis because human operators cannot process it fast enough.

    Pressure on utilities stems from two sources: a spike in electricity demand and a shift in how power is generated.

    An example of the operational strain can be seen at the regional level. With the recent deployment of artificial intelligence tools and high-performance computing, data centers require immense amounts of energy to operate. The largest power transmission utility in Texas recently reported a staggering 220 gigawatts of new connection requests, driven largely by a surge in AI and cloud-computing facilities, according to a CNBCreport.

    Alongside the rise in regional demand, global energy networks are absorbing an unpredictable variety of weather-dependent renewable energy such as wind and solar. The switch creates a volatile operating environment wherein supply and demand are balanced, second by second, to prevent blackouts.

    The challenges are compounded by the vulnerability of the grid’s physical and digital framework.

    More-frequent severe weather events cause costly disruptions, such as the devastating winter freeze that crippled the Texas grid and record-breaking heat waves that have overloaded transformers.

    Simultaneously, the energy networks’ digital architecture faces threats. As utilities replace outdated analog equipment with smart meters and control systems, they are increasingly vulnerable to cyberattacks.

    To overcome physical and digital vulnerabilities, grid reliability organizations, such as those conducting North American security simulations like GridEx, emphasize that the grid must become smarter, more agile, and completely automated. Energy researchers are noting that the key to this change lies in integrating AI across every layer of utilities’ operations.

    The AI imperative

    According to energy industry experts, using AI to manage power systems is no longer a futuristic research project; it has become a baseline operational necessity. Grid analysts emphasize that traditional grid-planning methods are too slow to handle rapid energy dynamics or to balance volatile renewable energy in real time within decentralized power systems such as microgrids.

    AI can fill the gap by processing vast amounts of data instantly. Machine learning algorithms can quickly analyze information from thousands of sensors, historical usage patterns, and weather forecasts to predict issues before they happen.

    An industrial digitization study conducted by McKinsey & Co. indicated that integrating advanced data and automation across infrastructure networks could reduce system design errors, decrease equipment downtime by up to 50 percent through predictive maintenance, and extend the lifespan of power machinery by up to 40 percent.

    From forecasting energy spikes to automatically fixing localized voltage drops, AI acts as the digital backbone of a self-healing grid, experts say. Deploying the complex systems requires a new workforce: power engineers who understand data science, as well as data scientists who understand electricity.

    Upgrading the Workforce

    To bridge the gap between groundbreaking AI research and practical field deployment, IEEE Educational Activities, in partnership with the IEEE Power & Energy Society, has launched the online Artificial Intelligence for Power and Energy Systems course program.

    The program explores core challenges threatening modern utilities. Rather than treating AI as an unverified black box that operates without human supervision, the curriculum focuses on safety, asset preservation, and strict reliability standards.

    The curriculum is designed to educate power system engineers, utility managers, and data scientists tasked with modernizing the grid. The program was developed by Fangxing “Fran” Li, professor of electrical engineering and computer science at the University of Tennessee in Knoxville and chair of the IEEE Working Group on Machine Learning for Power Systems.

    Five learning modules

    The program breaks down the technical transition into five modules that bridge high-level theory with real-world solutions:

    AI fundamentals.This module teaches engineers how basic machine learning models apply to power grids. It discusses how specialized neural networks solve complex power-flow calculations and how AI models can safely transition from computer simulations to physical, high-voltage equipment.

    Accelerating grid control. Learners are taught to leverage deep reinforcement learning, an AI approach that uses trial and error, to accelerate automated grid adjustments during emergency power events.

    Forecasting and data analytics.Using predictive modeling, engineers learn how to predict sudden demand surges, variable wind and solar outputs, and fluctuating wholesale electricity market prices to keep power affordable and available.

    Physics-informed and safe AI.To address trust—a barrier to utility AI adoption—this course covers AI models hard-coded to obey the laws of physics. The approach is designed to ensure that automated algorithms never make erratic choices that damage grid equipment.

    Generative AI and next-generation tech.Learners can explore the frontier of utility technology, including graph neural networks and large language models. This module highlights how generative AI can process complex, interdisciplinary data to streamline utility planning, emergency responses, and regulatory reporting.

    The algorithmic literacy and practical execution tools provided by the course program can help convert systemic risks into grid resilience.

    For individual access, visit the IEEE Learning Network. If you are looking for customized organizational options, contact a content specialist to discuss volume pricing.

12. Negotiating Your Salary Is About More Than Money

    

    This article is crossposted fromIEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity anddelivered to your inbox for free!

    Scroll through LinkedIn right now and you may find the same advice repeated by well-meaning people: “In a market this rough, just be grateful anyone will hire you. Take the offer.”

    I could not disagree more.

    Negotiating your offer is not ungrateful, and it isn’t greedy. Done well, it’s good for you and good for the company hiring you. I misunderstood this early in my career, and it cost me.

    I didn’t know it was an option

    When I got my first job in tech, I didn’t know negotiation was even on the table. The recruiter asked what salary I wanted, and I gave a number below the bottom of their range. They came back with the lowest number in their band—still more than I had asked for—and I was thrilled. I had no idea I’d left money on a table I couldn’t see.

    Then I started teaching at a Bay Area coding bootcamp in the evenings. A coworker mentioned what he made and it was nearly double my salary for roughly the same work. My jaw dropped.

    During that time, I began interviewing and got an offer. I handed in my resignation and my manager countered with an offer for nearly 30K more. That money had been there the whole time. At that moment, I realized my salary was a business decision, not a measure of my worth.

    What it looks like from the other side

    Years later, I became an engineering manager and saw the salary discussion from a different angle: A position would open. Many interviews later, we’d find someone we wanted, and HR would hand me a salary range to make an offer. I was encouraged to make an initial offer near the bottom to leave room for, you guessed it, negotiations.

    Most applicants didn’t negotiate.

    The first offer is rarely the ceiling. It’s usually the floor. Companies extend a reasonable number and quietly hope you say yes.

    It’s not all about the money

    Negotiating isn’t only about a bigger paycheck. (But who doesn’t want that?)

    Let’s say you’re on the job market, maybe recently laid off, and a low offer comes in. You take it out of relief. Then you start, you like the team, and you quietly resent the number. Now you’re stuck with it, and you’ll probably leave that role inside a year or whenever the market improves.

    Nobody wins there. You’re back on the market starting over, and the company loses someone good and pays more to replace you, when a fair number up front would have cost far less.

    Paying you fairly is cheaper than starting over.

    How to actually do it

    People overcomplicate this. Once I have an offer, I say some version of this:

    “Thank you so much for the offer, and I’m genuinely excited to join the team. I’m hoping we can come in around [10 to 20 percent higher than the original number]. Is there any wiggle room here?”

    Then I stop talking and let them respond.

    Why 10 to 20 percent and not double? The number you ask for is itself a signal. Ask for something wildly out of range and you’ve told them you never learned what the role pays, or that your expectations are miles from reality. That’s what makes a company walk away. A calibrated request reads as someone who knows their worth and did their homework.

    You’ve probably heard a horror story about someone who asked for more and had the offer yanked. Any company that would pull an offer over a reasonable question about pay is telling you exactly how they’ll treat you once you’re inside.

    If the salary can’t move, it isn’t the only lever. I’ve negotiated more remote days, a later start to drop my kids off, and a sign-on bonus when the base was locked. Most people negotiate none of these perks.

    You have more leverage than you think

    Negotiating can feel like something you can only do from a position of power. But if you’re in the final stages of an offer, you already have it. They want to hire you. They’ve spent weeks finding you. Now they’re hoping you say yes.

    That’s true even if you were recently laid off. Even if it’s your first job. Even if the number already looks higher than you expected.

    The game is being played whether or not you join in. Sit it out, and you’re not just leaving money on the table. You may be quietly shortening your own stay at a job you could have been happy in. So ask.

    —Brian

    The AI Arms Race in Technical Interviews Is Escalating

    If you’ve been on the job market for a software engineering role recently, you’ve probably encountered—or used—AI tools in the hiring process. From application filters to live interview assistants, both applicants and employers are trying to use generative AI to their advantage. Can real, human skills still shine through in this new reality?

    Read more here.

    This Graduate Student Equips NASA With Assembly Skills

    Sarah Downs, a Ph.D. student in electrical engineering at Texas A&M University, has long been interested in robotics and dreamed of working with NASA. This year, she achieved that dream, collaborating with NASA and the U.S. Air Force on an algorithm that enables satellites to insert an antenna into the correct spot.

    Read more here.

    IEEE Program Helps Girls in India See a Future in Stem

    Women make up only about 28 percent of the global STEM workforce, in part because of limited access to educational resources for preuniversity students—especially in areas like rural India. An IEEE initiative, the Women in Science, Engineering (WiSE) project launched to help expand opportunities and hands-on learning for young women.

    Read more here.

13. Siobahn Day Grady Wants Everyone to Be AI Literate

    

    Artificial intelligence is reshaping the skills employers expect from new graduates. In response, universities are scrambling to launch new courses, research centers, and industry partnerships that prepare students for today’s workforce. But building a cutting-edge AI curriculum demands funding and access to industry networks, resources that remain unevenly distributed across higher education.

    At North Carolina Central University, Siobahn Day Grady is trying to change that equation.

    In January 2025, Grady, an associate professor in the NCCU School of Library and Information Sciences, launched the first AI research institute at a historically Black college or university, or HBCU. The Institute for Artificial Intelligence and Emerging Research (IAIER) aims in part to help students and faculty across the university develop the skills needed to navigate a labor market increasingly transformed by AI.

    “There used to be a time where people could say, ‘I don’t do tech,’ or ‘That’s not for me,’” Grady says. “But we’re in a stage now where you do need digital skills. Now it’s evolving into AI literacy.”

    The approach reflects a broader shift in how many universities are thinking about AI education. AI skills are no longer confined to computer science and engineering departments—and at NCCU, they can’t be. The university does not yet have a dedicated computer science program, though it is developing one alongside a new AI minor.

    The challenge of providing these resources is especially acute for historically Black institutions. Although HBCUs account for roughly 3 percent of four-year institutions in the United States, they receive less than 1 percent of federal research and development funding, according to a 2025 reportby the Center for American Progress and the Thurgood Marshall College Fund. The same report found that 17 of the 43 federal agencies that distributed research funding to universities in 2023 awarded no funding to HBCUs.

    Yet less than two years since its launch, IAIER has emerged as a powerhouse for interdisciplinary AI education. Backed by a US $1 million Google.org grant, the institute has engaged more than 2,800 students, faculty members, and community residents through research initiatives and training. Now the challenge is sustaining that momentum to keep up with rising demand.

    “We have a guiding principle that we lead with on our campus,” Grady says. “AI is for everyone.”

    Why one research group wasn’t enough

    The mission to expand AI literacy grew out of Grady’s lifelong curiosity about technology. “I was born during a time [when] the internet did not exist,” she says. “Ever since the internet came to be, it’s changed our entire world.”

    Grady was particularly drawn to the questions tech raises about privacy, identity, and human behavior. After receiving her bachelor’s degree in computer science and master’s degrees in AI and information science, Grady pursued a Ph.D. in computer science at the North Carolina Agricultural and Technical State University to dig into those questions.

    Her dissertation focused on authorship attribution in social media, using machine learning and natural-language processing to determine whether a person’s writing style could reveal their identity. “I’ve always been intrigued by how much data we give for free,” Grady says. That work introduced her to the power of AI systems to detect patterns hidden within large datasets.

    “We have a guiding principle that we lead with on our campus: AI is for everyone.”

    After completing her doctorate in 2018, Grady joined NCCU as an assistant professor in the School of Library and Information Sciences. There, she researched machine learning applications for health care and autonomous vehicles. In 2020, she launched the Laboratory for Artificial Intelligence and Emerging Research at NCCU, giving students opportunities to participate in hands-on projects and explore AI beyond the classroom.

    Then in 2024, an opportunity emerged to apply for a Google grant, and Grady began thinking beyond a single research group. Rather than building another faculty lab, she envisioned an institute that could serve the entire university during the AI boom. “We wanted to capitalize on the moment and make sure we don’t get left behind,” Grady says.

    Since receiving the $1 million grant, Grady and her team have built a university-wide AI initiative, launched new academic programs, organized conferences, secured external support, and created research opportunities.

    “We’ve really operated like a startup,” Grady says.

    AI beyond computer science

    As part of the institute’s goal of integrating AI education across disciplines, all NCCU freshmen are required to complete an introductory AI course, designed in partnership with IBM, to build foundational prompting skills. The institute has also worked with faculty development teams to help instructors integrate AI into their teaching.

    Research is another part of the strategy. IAIER has awarded seed grants of up to $10,000 to faculty members exploring AI applications across departments. The first cohort funded 11 projects spanning social work, digital archiving, health care, and information science. One project, for instance, is creating an AI lab where students in social work courses can practice client interactions through simulations.

    “It’s really interesting to see the lens that our researchers take in trying to solve complex problems and also bring our students along with them,” Grady says.

    The institute’s growth has been fueled by a mix of workforce training, interdisciplinary research, and, especially important, industry engagement. “Industry is where the advancements are really moving at that very fast rate,” Grady says, “not necessarily higher ed.”

    To bridge that gap, IAIER hosts events that connect students and faculty with researchers, employers, and technology leaders. It has held sessions with companies including Deloitte, FICO, and Anthropic. Partnerships with Google and IBM let students gain recognized certificates and credentials. And last year, the institute hosted the first OpenAI Academy Summit held at an HBCU, drawing 444 participants from more than 40 institutions.

    Sustaining the vision

    The institute’s rapid growth has created a new challenge: continuing its momentum.

    “Funding right now is the biggest barrier for [IAIER] to remain sustainable,” Grady says. As interest in the institute continues to grow, demand for its programs is beginning to outpace its capacity. “People just want more,” she says.

    The bottleneck reflects a broader tension across higher education. AI is evolving quickly, while developing new academic programs, training faculty, and building research capacity takes time. The uncertainty is compounded by a shifting political landscape. As a whole, U.S. universities are grappling with proposed cuts to federal research spending and increased scrutiny of diversity-focused initiatives under the Trump administration. However, in September 2025, the administration also announced a $500 million one-time investment in HBCUs and higher-ed institutions chartered by Native American tribal governments.

    Meanwhile, NCCU has continued to attract new investment. Last September, in a collaboration with Howard University and two other institutions, IAIER received a nearly $500,000 award through a National Science Foundation research coordination network program to help define emerging AI jobs, identify in-demand skills, and inform future credentials and curricula. That work will continue this fall when IAIER opens its first dedicated physical space on campus, Grady says.

    Over the next several years, Grady plans to expand academic programming, launch the university’s computer science major and its AI minor, increase faculty research opportunities, and integrate AI more deeply across campus operations. She also plans to deepen the institute’s collaborations with industry partners.

    Beyond program expansion, Grady sees the institute’s long-term success as linked to building a model other universities can adapt. “We’re creating a framework that can help not only HBCUs,” she says, “but also help any university looking to do similar work.”

14. Laboratoria’s Mariana Costa Empowers Women in Tech

    

    In shaping her career, Peru native Mariana Costa has asked herself a question: What can I do to make life better for women in Latin America?

    The answer she landed on was training them for tech jobs.

    Mariana Costa

    Employer

    Laboratoria

    Title

    Co-founder and president

    Alma Maters

    London School of Economics; Columbia

    Such positions pay well and are in demand. And for too long, women across the region have been locked out of them, she says.

    Costa is president of Laboratoria, a U.S.-registered nonprofit based in Miami that she helped found. Laboratoria has trained thousands of women in 11 Latin American countries for technology careers. She has built training centers in the countries and has placed graduates at major companies. Meanwhile, she has become one of the most recognized voices in the region on workforce equity and tech education for women.

    IEEE recognized her work with its President’s Award this year for her “distinguished leadership and contributions to the betterment of society.” Recipients of the award are selected by the IEEE president with the consent of the IEEE Board of Directors.

    Costa says the recognition came as a surprise because she is not an engineer by training and had never considered becoming affiliated with IEEE.

    She was presented with the award at the IEEE Honors Ceremony on 24 April in New York City.

    Peru: a country of contrasts

    Costa grew up in Lima, Peru’s capital, in a household with no connection to engineering or technology. Her mother was an art historian and professor, and her father was a lawyer. The family was financially comfortable and traveled abroad regularly. Costa attended well-resourced schools.

    That economic stability came with a reckoning, Costa says, in that she recognized early on that economic inequality had created separate societies inside Peru. Her parents, she says, made it “clear that my reality wasn’t the reality of most people in my country.”

    Lima is a microcosm of the country, she says. The divide in the capital city is visible: A kilometers-long concrete wall topped with barbed wire separates wealthier neighborhoods from shantytowns, where residents lack running water.

    Nationally, there are splits along ethnic and geographic lines. The highland and jungle regions remain home to mostly indigenous communities with limited educational access and a deep cultural distance from the Hispanic-dominated coast.

    The questions that stirred in her as a child never left, she says.

    “Why do I live in a country where so much depends on where you’re born?” she asked herself. “What does it mean to be Peruvian when individual realities are strikingly different?”

    Those questions followed her to the London School of Economics, where she studied international relations, graduating with a bachelor’s degree in 2007. She held onto the questions when she moved to Washington, D.C., where she spent the next four years working for the Organization of American States, helping Latin American governments improve public services that fall under the heading of civil registration.

    “I said, ‘How can it be? The tech space has so many rich opportunities. Why aren’t any women here?’”

    The OAS Universal Civil Identity Program in the Americas provides technical support to national civil registry institutions, modernizing them to foster social inclusion and ensuring the right to civil identity for all people. Without civil identity, a person can’t access education, health care, legal employment, social services, or the right to vote. People without the classification don’t exist in the eyes of the government. They also can’t own property, get married officially, or pass citizenship rights to their children.

    Doing that work deepened her concern about the socioeconomic disparities in her homeland, she says. In search of practical solutions to those problems, she went to New York City in 2011 to further her education. She earned a master’s degree in public administration and development from Columbia in 2013.

    Technology was not yet part of a solution. But Costa already had met someone who would change that.

    Falling in love with a programmer

    While working in Washington, Costa met Herman Marìn, a software engineer who used digital tools in support of social causes. Because he was doing work she had never associated with programmers before, her assumptions about the field dissolved quickly.

    “I had a vision of [programmers doing] something not very social—strictly technical,” she says. “And my then-boyfriend, now husband, actually worked for different social movements that used technology to address social causes.”

    That realization cracked something open, she says: “I said, ‘Oh! Technology can actually be a tool to address some of the more stubborn problems in our societies.’”

    After earning her degree at Columbia, Costa returned to Lima with her husband. She had been abroad for nearly a decade and felt the pull of home.

    “The thought of not moving back to my country was something that tormented me a bit,” she says. “I really felt I had to move back, at least to try it out and contribute somehow.”

    What Latin America’s tech space lacked

    When Costa, her husband, and a friend from graduate school moved to Lima, they had modest savings and big ambitions. They wanted to build something that combined technology with social impact.

    They started with what they had: a small digital services agency, where they built websites for clients.

    The business grew, and they hired more employees. Their team expanded to a dozen software engineers. And as it did, Costa noticed three things.

    First, there weren’t enough trained developers to meet the demand. Second, many of their best hires did not have traditional computer science degrees. Some had never even finished college.

    “There was no other space where you could actually build an amazing career and get a well-paying job without a good degree from a good school,” she says. “The tech world was different. It was open in ways other fields weren’t.”

    Thirdly, she noticed that there were no women on the team. In the first six months, Costa says, they didn’t interview a single female developer.

    Her colleagues shrugged. It’s just how it is, they told her.

    Costa, the outsider, didn’t accept that.

    “I said, ‘How can that be? The tech space has so many rich opportunities,’” she says. “‘Why aren’t there any women?’”

    Building Laboratoria

    In 2014 she decided to launch Laboratoria. The business model was simple: Find talented women who hadn’t yet broken into tech, train them quickly on practical skills, and connect them with employers who needed developers.

    Laboratoria started offering a six-month immersive boot camp that covered Web development, UX design, data literacy, strategic use of artificial intelligence, and soft-skills coaching such as interview prep and projecting confidence.

    Just as important for career success, Costa says, is a user-centered mindset. She says Laboratoria’s program emphasizes the discipline of keeping the client’s needs in mind when designing the work.

    The teaching model has evolved beyond the boot-camp structure, but the organization still focuses on helping Latin American women develop tech skills and land quality jobs in the digital age. These days, the training, conducted via twice-weekly live Zoom sessions, lasts six weeks.

    “We needed developers ourselves,” she says of the company’s original logic. “I said, ‘Why don’t we run a program to train women—women who are incredibly talented but haven’t been given a chance yet—and help them gain the skills they need to get a great job as quickly as possible?’”

    Mariana Costa [seated, right] poses with Laboratoria co-founder and CEO Gabriela Rocha and co-founder and chief product officer Rodulfo Prieto.Valeria Martens

    It worked. Laboratoria expanded from Lima to Santiago, Chile; Mexico City; São Paulo, Brazil; and Bogotá, Colombia. The organization eventually incorporated as a nonprofit in the United States. Today its programs are held remotely in Latin America’s major job markets. So far, Laboratoria has opened the doors to tech careers for more than 3,500 women.

    Costa says she believes the most important skills Laboratoria’s graduates need aren’t purely technical. Close behind the growth mindset is self-confidence, she says.

    “Knowing who you are, valuing who you are, and learning to trust yourself and your capacities are indispensable traits,” she says.

    Networking, she adds, is the third pillar, and often the hardest to build for women without access to elite schools or flexible work schedules.

    “When you go out in the market,” she says, “you realize that having a network of people who trust you and know your work is such a valuable and critical asset.”

    IEEE: a new connection

    Costa’s introduction to IEEE came late—but it landed hard.

    She is not an IEEE member, so when she was contacted this year about receiving the President’s Award, she did her homework on the organization. What she found, she says, was a public charity whose reach and values aligned with her mission.

    “IEEE is about expanding access to opportunities in the world of technology,” she says. “And that’s also the core of what we do at Laboratoria.”

    She says she also sees IEEE as a living example of something her company preaches every day: “I was talking about the value of professional networks, and I think IEEE is such an amazing reference in that space. It exemplifies this belief that human connection—not only doing your work but also sharing and learning with others—is at the core of building thriving technology careers.”

    The engineering organization found her well after she launched her tech-related career. But it wasn’t too late. She says she intends to make the most of the connection.

15. 2022 IEEE President K.J. Ray Liu Honored for His Leadership

    

    Unlike many budding engineers, K.J. Ray Liu wasn’t inspired to enter the field by tinkering with electronics or following in the footsteps of a family member. Growing up in Taichung, Taiwan, he answered his government’s call for students to become electrical engineers to help manufacture semiconductors in the 1970s, when the country’s economy was struggling.

    “Students who were good in math, science, and physics all wanted to be an electrical engineer because that was the top priority of the government,” Liu says. “That’s how I got into engineering. Now Taiwan is a world leader in semiconductors.”

    K.J. Ray Liu

    Occupation

    Retired professor of information technology and a digital signal processing researcher at the University of Maryland in College Park

    Member grade

    Fellow

    Alma maters

    National Taiwan University; University of Michigan; UCLA

    But by the time he graduated from university in 1983, semiconductor facilities were still under construction, so there were no jobs available.

    Instead, he went on to have a successful career as an educator and entrepreneur in the United States.

    For 31 years, he was a professor of information technology and a digital signal processing researcher at the University of Maryland in College Park until he retired in 2021.

    Liu was the chairman, CEO, and CTO of Origin Wireless, a startup he founded in Rockville, Md. Origin, which was acquired by ADT in February, pioneers artificial intelligence for wireless sensing and indoor tracking.

    Liu, an IEEE Fellow, is an active IEEE volunteer who served as the organization’s president in 2022.

    IEEE honored him with this year’s Haraden Pratt Award for “transformative and impactful leadership.”

    Liu is credited with increasing the diversity of nominees for IEEE’s Fellow program, which is the highest level of membership. He also led the effort to realign the organization’s regions geographically to ensure more equitable global representation on the IEEE Board of Directors.

    He received the Pratt honor on 24 April during a ceremony in New York City. The IEEE Foundation sponsored the Board-level award.

    “More than anything, I share the honor with the volunteers and staff I had the privilege to work alongside,” he says. “Our hard work is fueled by our shared devotion to this professional home we love and care for so much.”

    Making the switch to signal processing

    In the 1970s, Taiwan’s policymakers decided to improve the country’s economy by pivoting from making products such as shoes and umbrellas to manufacturing electronics.

    The industry got its start in 1976 when RCA, a major electronics company at the time, agreed to transfer licensed semiconductor processes to Taiwan’s Industrial Technology Research Institute. ITRI spun off several semiconductor-related companies including the Taiwan Semiconductor Manufacturing Co. TSMC, launched in 1987, is the world’s largest dedicated semiconductor foundry.

    Liu graduated in 1983 with a bachelor’s degree in electrical engineering from National Taiwan University, in Taipei. At the time, there were no semiconductor companies to work for, he says.

    “Nowadays, many of the country’s university graduates go right to TSMC to get a job,” he says. “But back then, there was no real job market.

    “Most of my classmates—including me—came to the U.S. for graduate studies. Many of us stayed and, over the last three to four decades, contributed to the development of electronic computer communication technology in the U.S.”

    Liu left Taiwan after a two-year mandatory stint in the Republic of China Armed Forces to attend the University of Michigan, in Ann Arbor, where in 1987 he earned a master’s degree in electrical engineering.

    “If I can help make IEEE a better professional home for future members, that is something that I can pay back to IEEE.”

    He went on to earn a Ph.D. in electrical, electronics, and communications engineering in 1990 from the University of California, Los Angeles. His interest in digital signal processing and very-large-scale integration (VLSI) was sparked while at UCLA. Today VLSI powers all modern electronics.

    “When I was a graduate student, there was no wireless communication. Everybody had a landline,” he explains.

    VLSI was an important, active research field at the time.

    “My research interest was digital signal processing,” he says. “One day I saw a book on VLSI signal processing on my professor’s bookshelf. I immediately thought to myself: That is the field I want to pursue.

    “VLSI is one lane, digital signal processing is the other, and there is a bridge linking the two. I was interested in both areas, so I did my Ph.D. thesis on VLSI signal processing.”

    After graduating, Liu joined the University of Maryland, where he is credited with establishing its signal processing research program.

    In addition to teaching, he conducted research on a broad range of signal processing and communication aspects. The topics include bioinformatics, game theory, signal processing algorithms and architectures, and wireless sensing and communications.

    He has authored more than 10 books and 900 papers, and he holds 250 patents. You can find his research papers in the IEEE Xplore Digital Library.

    Ambient-sensing trailblazer

    Liu is considered to be a pioneer in the field of ambient sensing. The technology gathers environmental data and is used in security systems and health-monitoring devices.

    He came up with the idea, he says, while working on a project in 2009 for the U.S. Navy. He was trying to solve a problem the Navy was having with the wireless communication systems used in its submarines. Because submarines are made of metal, radio waves were unable to penetrate the vessels’ compartments and instead bounced around, creating interference, he says.

    His solution was to use a relatively unknown concept in physics: time-reversal signal processing. The technique captures waves, such as sound and electromagnetic signals, and sends them back through the same medium in reverse, flipping the signal from last-in to first-out, and re-emits them.

    “By using time-reversal feed, we could increase the signal-to-noise ratio by four times,” he says. “That improved performance dramatically.”

    He became fascinated by the physics of time-reversal signal processing, he says, and wondered how he could apply the concept to serve society. After three years of research, he came up with the idea of using wireless sensing applications through ambient radio waves from surrounding Wi-Fi networks.

    “I learned to turn Wi-Fi networks into sensing networks that decipher our activities,” he says. “We could know everything happening around us—our motions, breathing, heartbeat, even fall detection—without any wearables.”

    Through the university’s incubator, which encourages faculty to work on projects with an impact on society, he launched Origin in 2013. The company’s Wi-FI and AI sensing technology enables accurate indoor tracking, motion detection, and health monitoring without the need for wearable devices or cameras. Its products, including its remote patient monitoring, received three innovation awards at the 2020 and 2021 Consumer Electronics shows, including one for best innovation.

    Finding his professional home

    Liu joined IEEE in 1986 as a graduate student to access its research papers, he says.

    “If you didn’t join an IEEE society, you didn’t get its journal—which meant that you couldn’t read the most up-to-date research papers,” he says. “So, I joined the IEEE Signal Processing Society. When I attended my first signal processing conference, I knew I had found a professional home. I met many like-minded people, and together, we built a professional home for our members worldwide.”

    He became an active volunteer, holding top leadership positions including 2012–2013 president of the Signal Processing Society and 2016–2017 director of IEEE Division IX, which covers societies focused on signal processing, data transmission, navigation, and transportation. In 2019 he was vice president of the Technical Activities Board.

    In 2022 he served as IEEE president and CEO. The three accomplishments during his term he says he is most proud of are increasing the prize money for the IEEE Medal of Honor, overseeing the realignment of IEEE regions, and establishing greater financial transparency.

    The reason for increasing the prize for IEEE’s highest award—from US $50,000 to $2 million—in 2025, he says, was to underscore the importance of the technologies the IEEE community develops. Those innovations include semiconductors, the Internet, and the GPU. The money for the Medal of Honor now exceeds that of the Nobel Prize, which carries an award of roughly $1 million.

    “We need the whole world to understand the IEEE community has made the most impact on society in the last century,” Liu says. “Nevertheless, we did not receive the attention and respect we deserved, so we needed to help ourselves. We want the whole world to know what our contributions are.”

    His next achievement was realigning IEEE’s regions. During the past several years, membership in Region 10, which covers countries in Asia and the Pacific, has grown from 10 percent of total membership to nearly 40 percent, he says. It is the largest and most populous of IEEE’s geographic areas, but its members were not equitably represented on the Board of Directors. Each region had one representative on the Board.

    “The region has 40 percent of the members but only makes up 10 percent of the Board,” Liu says. “That didn’t make sense to a lot of us.”

    The IEEE Board in 2022 approved region realignment. The total number of regions remains at 10, but their organization is changing. Effective 1 January 2028, the six U.S.-based regions will be consolidated into five, and Region 10 will be split into two. IEEE will no longer use the Region 1 designation. The new Region 2 will represent the Northeastern and Eastern U.S. Region 10 will cover North Asia, and the new Region 11 will represent South Asia and the Pacific.

    Liu also succeeded in leading a movement that persuaded the IEEE Board to invest in a better financial reporting system to have a clearer understanding of the organization’s finances. A more modern system now tracks banking transactions, contracts, expense reports, and other spending.

    “Now we know exactly where the money comes from and where it is spent,” he says, “so that we can make more informed decisions.

    “If I can help make IEEE a better professional home for future members, that is something that I can pay back to IEEE,” he adds. “I truly appreciate what IEEE offered me. From student to professor to an established leader, at every stage, it offered me different opportunities to grow. That is why I worked very hard when I was president to make sure everybody realizes it is a professional home for our entire career.”

16. IEEE Program Helps Girls In India See a Future In STEM

    

    Roughly half the world’s population is female, but the STEM fields don’t reflect that. The 2024 U.N. Global Education Monitoring Report on gender found that about 35 percent of STEM college graduates were women. The proportion hasn’t increased much in more than a decade.

    When it comes to STEM careers, the percentage is even lower. Women made up about 28 percent of the global STEM workforce in 2024, according to the World Economic Forum.

    There are myriad factors for the discrepancy, including a lack of family support, some teachers encouraging only boys to pursue STEM subjects, and a shortage of female role models.

    But one force is at play long before college majors are ever considered: limited access to STEM-focused educational resources for preuniversity students. Especially for students in rural communities, the limited access curtails curiosity in STEM subjects before interest can take root. Although the lack of opportunity impacts boys and girls alike, when combined with other factors it can have an outsized effect on girls in some rural regions.

    IEEE Fellow Rajiv Joshi is one of the creators of the Women in Science, Engineering project. He is a principal scientist and master inventor at the IBM Watson Research Center, in Yorktown Heights, N.Y.Rajiv Joshi

    One such place is rural India. The challenges of pursuing a STEM education—or any education at all—increase sharply as rural Indian girls move into their teen years. Social barriers including early marriage, traditional gender roles, and familial expectations for financial support contribute to girls’ dropping out of school, according to the Mahadev Maitri Foundation, a nongovernment organization focused on childhood education in underdeveloped areas. Dropout rates for girls spike between the ages of 11 to 14, according to the foundation.

    The trend is something IEEE Fellow Rajiv Joshi and IEEE Senior Member Rajesh Zele want to change. A shared passion to keep rural Indian girls from dropping out of school and on paths to STEM careers led them to launch the Women in Science, Engineering (WiSE) project.

    “Talent is universal, but opportunity is not,” Joshi says. “WiSE is one way to expand opportunities.”

    Bringing the WiSE vision to life

    Joshi, vice president of industry for the IEEE Circuits and Systems Society (CASS), is a principal scientist and master inventor at the IBM Watson Research Center, in Yorktown Heights, N.Y. Zele is a professor of electrical engineering at the Indian Institute of Technology Bombay (IIT-B), in suburban Mumbai.

    They presented their proposal for the three-year initiative to the society’s board of governors in 2022 and received a grant of US $80,000.

    The framework

    WiSE was a five-day, hands-on learning program held on the IIT-B campus. Starting in 2023, it ran for three years and was held during the last week of March. A new cohort of 160 to 200 girls from rural and tribal areas in the states of Maharashtra and Karnataka attended each year.

    The event was divided into two parts: hands-on learning through Break-Make-Program (BMP) experiences and presentations by influential female Indian role models.

    Zele, project manager Arti Auti, several IIT-B faculty members, and about 70 student volunteers from the institute oversaw the program.

    Selecting the first cohort

    With funding secured, Zele and his team on the ground in India got busy selecting attendees for the inaugural 2023 class. They reached out to administrators at 68 schools in Maharashtra and Karnataka. Although the two states are among the most urbanized in the country, each has vast rural areas where educating teen girls competes with early marriage and familial support pressure.

    Teachers identified girls with high scores in mathematics and science, and community leaders recommended students who could benefit from the program.

    Then outreach to their parents began. The adults were required to commit their own time, not just grant permission for their daughters to attend. They participated in quarterly online meetings that included the girls’ teachers, IIT-B student mentors, and other program volunteers after the week concluded. The check-ins held parents accountable for supporting their daughters’ continuing school attendance. The commitment to join the meetings was to last for at least four years after their daughter’s program participation ended.

    Hands-on learning is key

    Participants stayed in one of the institute’s dormitories for the week and attended sessions held Monday through Friday.

    They worked together in small teams to build things rooted in STEM concepts. The teams were supervised by Arti, IIT-B faculty, and student volunteers.

    “The idea behind BMP,” Joshi says, “was to give the girls an opportunity to take a gadget apart, then rebuild it, perfect it, or come up with a totally new idea.”

    The sessions included working with bioluminescence and bacteria (introducing participants to biology and microbiology), learning about autonomous underwater vehicles, and building a remote-controlled robot. The robot construction was the capstone event, allowing the girls to combine the mechanical and electronics engineering skills they’d practiced throughout the week.

    The build kits were created by students in Zele’s Advanced Integrated Circuits and Systems Lab. The girls and teachers were allowed to take the kits home to keep the learning going and spread STEM awareness.

    “Many girls used those kits at different events to demonstrate their STEM skills to others,” Joshi says. One was Sushi Pawar, who built a drone during WiSE, then went on to demonstrate it to a national audience.

    “I presented the drone project in 2024 to Prime Minister Narendra Modi during the Pariksha Pe Charcha,” Pawar says. That initiative is an annual event open for students in classes 6 to 12, their teachers, and parents. The focus is on helping students manage stress during exam time through fun and celebratory activities. The event is supported by the Indian Ministry of Education and hosted by the prime minister.

    Each year, millions of students complete an online multiple-choice test to qualify to attend in person and meet the prime minister. In 2024 nearly 4,000 participants attended.

    Inspiration as the foundation

    WiSE was about more than hands-on learning. It also included daily presentations from “Winspirers”: Indian women who achieved success in their lives or had STEM careers. They included scientists, doctors, military officers, professors, and other women who overcame obstacles.

    The first such speaker was Savita Dakle, a farmer from Maharashtra who dropped out of school after 10th grade, got married, and had two children.

    Despite limited farming knowledge, she learned quickly and built a coalition of 400 female farmers from her village. She taught them how to use mobile phones and social media to share information and resources.

    Today, that coalition has more than 1 million members in two online communities. They share tips on market pricing, growing crops, and more.

    Zele says he views Dakle as the ideal Winspirer: someone who built a thriving business with few resources and no academic or professional credentials.

    Dakle mirrors the challenges faced by many program participants, he says, noting that many girls face pressure to leave school early to marry or financially support their family. She is living proof, he says, that no girl’s circumstances define her possibilities.

    The practical impact of WiSE

    No empirical data yet exists to measure the project’s success, but feedback from participants shows the program has made a difference. Many from the 2023 cohort remained in school and are now pursuing higher education.

    Participant Tejswini Manoj Patil credits the initiative with boosting her confidence in science and math.

    “Before WiSE,” she says, “I was interested in STEM but felt intimidated by the complexity of the subjects. After participating, my interest shifted from passive curiosity to active confidence. The hands-on projects showed me that I am capable of solving real-world problems.”

    Meeting female role models was important, attendee Ritu Ravindra Patil says, adding: “WiSE completely changed my perspective, and meeting the Winspirers inspired me to aim for higher education.”

    The project opened career perspectives for some.

    “Before WiSE, I wanted to be a doctor,” Pallavi Bharti says. “I believed engineering was very stressful and boring. When I joined the program and explored IIT-B, I was amazed. Everyone was so friendly and supportive, and the passion in students for their work motivated me. All those experiences gave me confidence to take math classes and become an engineer.”

    What’s next?

    The initiative ended last year, but the framework it built lives on. Groups with similar goals have adopted parts of the concept, Joshi says.

    The IEEE CASS chapters in Bangalore and Kerala are likely to be among the first to advance the initiative’s ideas, he says.

    Alex James, an IEEE senior member and founding chair of the IEEE CASS Kerala chapter, has adapted the framework for use in his region, Joshi says. James is a professor of AI hardware and a dean at Digital University Kerala in Thiruvananthapuram.

    Joshi guided James as WiSE concepts were implemented.

    IEEE Senior Members Jayesh Tanwani and Suman Dwivedi from the IEEE CASS Bangalore chapter are likely to bring program concepts into their work, Joshi says.

    Tanwani, chair of the Bangalore chapter, is a system-on-a-chip design engineering manager at Intel in Bengaluru. Dwivedi is a senior manager at Synopsys in Bengaluru. They are bringing STEM outreach to remote schools in Karnataka.

    Joshi and Zele say they hope to see the concept expand beyond India.

    “We want to spread this across other continents and see how we can integrate WiSE into new or existing initiatives in those places,” Joshi says. He says he has received requests from countries in Asia, Africa, and Europe for information on WiSE.

    Both say awareness and outreach are key things that IEEE CASS chapters around the world are well positioned to support.

    “We want to make a difference in young women’s lives,” Zele says. “It’s all about giving attendees the skills to stand on their own feet and have the information to make good decisions.”

17. This Graduate Student Equips NASA’s Robots With Assembly Skills

    

    Like many engineers, Sarah Downs says she knew she wanted to pursue a STEM career from a young age. As a teenager, she discovered robotics through her Tulsa, Okla., middle school’s First Lego League team, and she fell in love with the field, she says. Downs participated in the international robotics program from 2014 to 2016.

    Watching PBS specials on NASA Mars rovers Spiritand Opportunity, and seeing the live broadcast of the Curiosity rover launch in 2011, inspired the teen to dream of a career working with NASA.

    Sarah Downs

    MEMBER GRADE

    Graduate student member

    UNIVERSITY

    Texas A&M University in College Station

    MAJOR

    Electric engineering

    This year the IEEE graduate student member achieved that dream. For her final project as a master’s degree candidate in electrical engineering at the University of Tulsa, she worked on an algorithm in collaboration with NASA and the U.S. Air Force.

    The algorithm she developed enables a robot assembling satellites in space to insert an antenna into the correct spot, addressing robotics’s classic peg-in-hole problem of inserting an object into its corresponding hole.

    Now a Ph.D. student in electrical engineering at Texas A&M University in College Station, Downs is continuing her research on satellite assembly and manipulation “but on a much larger scale,” she says.

    Following a childhood passion

    Downs grew up in the Tulsa area. Her father, who died from a heart attack in 2015 when she was 13, was a safety advisor in the oil and gas industry. Her mother stayed home to take care of her brother, who has autism. After her father died, her mother went back to college to earn a bachelor’s degree in business so she could support the family.

    “We didn’t have much income, and my mom was always worried about money,” Downs says. “That made me more aware of having a successful career, in a monetary sense.”

    From then on, whenever she considered her future career, having a decent salary to support the family was high on her list.

    By pursuing a career in robotics, she says, she can follow her passion while obtaining financial security.

    In high school, Downs joined the First robotics club, where she found herself drawn to the electrical components used in the machines she and her classmates built.

    During her final two years of high school, she participated in an extension program at Tulsa Tech, a training school. She spent half her day in high school classes and the other half taking engineering courses at the vocational school.

    After graduating in 2020, she accepted scholarships to attend the University of Tulsa. She began her freshman year at UTulsa not knowing whether she wanted to major in electrical or mechanical engineering, she says, adding that her love of working with small systems helped her choose EE.

    For her senior year capstone project, she and two of her classmates designed a lunar lander exhibit for the Tulsa Air and Space Museum. They created an interactive game that simulates missions on lunar and martian surfaces. Four celestial bodies—the moon, Venus, Mars, and Titan—are listed across three computer monitors. Using a game controller, museum visitors can explore the virtual surface of each one. The exhibit is still on display.

    Downs earned her bachelor’s degree in electrical engineering in 2024 and continued her education at the university’s EE master’s degree program.

    Both more and less complicated than people think

    When Downs began her graduate studies, she was supposed to be part of a NASA robotics project for two years. But when a delay in government funding postponed the project’s start, she instead spent her first year in the school’s Institute for Robotics and Autonomy, then newly launched. Its main focus is developing robots to assist people who have mobility challenges.

    Inspired by her grandmother, who was wheelchair-bound due to severe arthritis, Downs developed a robotic arm that helps older people and wheelchair users live independently. The arm was able to identify and place objects in the appropriate locations inside the home, such as unloading certain groceries from a shopping bag and placing them on a shelf or in separate containers.

    Before the start of her sophomore year in 2025, the NASA project finally secured government funding. She developed a robot that achieves the peg-in-hole task without using any vision systems. Typically, cameras help guide robots’ satellite-assembly work. But in the harsh, remote environment of outer space, cameras might malfunction or encounter delays.

    “Don’t stop asking questions. Especially in engineering, don’t pretend like you know everything, because science is about constantly wanting to learn and listen.”

    Rather than using cameras, Downs’s robotic arm deploys a force-based insertion process to sense position and orientation of objects in the arm’s environment. The robot loosely grips an antenna and, with a torque sensor on its gripper, “feels” the force feedback of where the satellite and antenna are in relation to each other. The robot then guides the antenna assembly into a target opening on its satellite and maintains the position during adhesion.

    Adding to the complexity, the robot performs its task in zero gravity.

    “Without gravity, you now have to consider the arm’s reaction torques on the satellite to avoid flinging it into space,” Downs says. Any motion from the arm during the insertion process, especially from increased forces, could cause the satellite to continue movement in that direction.

    To combat that, Downs is performing calculations for the project to direct targeted reverse thrusts and counter the force of the robot’s motions.

    Her graduate project captures the simple yet complex nature of robotics that she finds fascinating, she says.

    “I think robots are both more and also less complicated than people think,” she says. “Really, all you need to start programming a robot is its Denavit-Hartenberg parameters, and you can do a lot with that,” she says, referencing the four values used to describe the position and orientation of a robotic arm and manipulators. Even with different grippers and degrees of freedom, “fundamentally, all robot manipulators start there,” she says.

    “But,” she adds, “we’re still learning so much about how robots interact with their environment. Even something simple to us, like manipulating a pen, is still incredibly complex for robots.”

    Downs is completing her doctoral thesis in the Robotic Space Simulator project at Texas A&M’s Robotics and Automation Design (RAD) Lab, which specializes in developing machines that can survive in extreme environments. It collaborates with NASA.

    Her thesis advisor is Robert Ambrose, a NASA veteran who launched the RAD Lab in 2022. The IEEE member is set to serve as associate director of the school’s Space Institute, due to open this year in Houston. The research facility is being built next to the Johnson Space Center.

    After earning her Ph.D., Downs says, she hopes to one day work for NASA, developing rovers that collect samples from Mars or robotic arms that perform tasks on space stations.

    To learn more about robots, check out IEEE Spectrum’s guide.

    Getting out of the engineering bubble

    Downs joined IEEE in 2020 as a freshman at UTulsa to get more involved in electrical engineering events on campus. At the time, the COVID-19 pandemic kept clubs and organizations from meeting in person.

    She was active in her school’s IEEE student branch and was elected as its 2022–2024 president. Under her leadership, the branch went from having a few events to hosting one every two weeks.

    They included lunch-and-learn sessions and dinners that connected students with professional engineers and the university’s alumni. Downs also organized hands-on workshops on soldering, 3D printing, CAD modeling, and résumé-building.

    Her efforts helped increase the branch’s executive board membership from roughly five students to 25 in 2023. The same year, her soldering workshop attracted about 80 students.

    She says she enjoyed working with IEEE, especially “engaging with alumni and learning from engineers.”

    IEEE is a great resource for networking opportunities, she says, noting that “during the COVID-19 pandemic, engineering students stayed in their bubbles.” IEEE events helped the students make connections that could serve them well, she says.

    “Networking is very important, especially in today’s tough job market,” she says. “It’s a lot about who you know and how people observe your work ethic.”

    Downs, who now serves as an IEEE graduate advisor for UTulsa’s student branch, says she has seen firsthand how the school’s student branch network has benefited its student members.

    “A lot of them have found jobs” because of IEEE, she says.

    The working and networking of an engineer

    As the IEEE graduate advisor for UTulsa’s student branch, Downs noticed that many engineering undergraduates finish college without any hands-on experience, whether it be a project or an internship.

    “Their résumés are very sparse, and they have no proof of their technical skills,” she says. She herself completed a facilities engineering internship at Tulsa International Airport’s American Airlines maintenance facility after her sophomore year of college. And she was an electrical engineering intern at Flight Safety International outside Tulsa after her junior year and after she graduated. The company designs, builds, and maintains its own flight simulators.

    Her advice to undergraduates is to hone and demonstrate both their hard and soft skills by working on research projects or even personal passion projects.

    “A Raspberry Pi doesn’t cost that much, and you can start working with that immediately,” she says. Students also can take part in engineering interest groups and professional organizations at their school, she adds.

    “Put yourself out there and join a research team,” she says. “It’s a great way to show people that you’re a good person to work with and you’d do a good job in the field.”

    She adds that it’s also a fine way to keep learning—which is what drew her to a field that has developed only within the past century.

    “We’re still constantly learning about robots,” she says.

    “Don’t stop asking questions,” she advises students. “Especially in engineering, don’t pretend like you know everything, because science is about constantly wanting to learn and listen.”

18. When Career Risks Are Worth Taking

    

    This article is crossposted fromIEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity anddelivered to your inbox for free!

    Before we get into this week’s article, I’d love to hear from you. If you have a question about your career or an upcoming decision that you want advice about, you can ask it here. I’ll be reading through your responses and picking questions to answer on a regular basis. Now back to our regularly scheduled program.

    The Safest Career Move Is Often the Riskiest

    Software engineers have some of the shortest tenures of any white-collar profession. The average software engineer stays at a company for roughly two years, about half as long as workers in most other knowledge professions. The layoffs of the past few years have certainly highlighted this instability, but it was already there.

    This isn’t an essay about a broken job market though. Rather, it’s about how to turn that instability to your advantage, which is something I’ve spent the last decade doing on purpose.

    Playing It Safe Was the Riskiest Option

    I switched careers into software in my 30s. I had a stable job at a community college, complete with a union and a pension. It was about as secure as a career gets, and I learned to program on the side.

    Then I did something nearly everyone in my life considered reckless: I quit, leaving the secure job to become a junior developer at 31. My own mother was skeptical. I took the riskier job anyway, for two reasons: It was the work I actually wanted, and I could see potential.

    My first development job was at a grocery retailer. Good people and a company I liked. But I kept meeting engineers earning twice my salary for the same work. In the San Francisco Bay Area, surrounded by some of the best engineering talent in the world, I realized my skills were stagnating.

    So I left for a small startup. I learned more in nine months than I had in the previous two years, and my salary doubled.

    Over the years I’ve come to treat career risk as something to manage deliberately. It falls into two categories.

    Take Risks With Your Job

    The first type of risk involves the job itself: Bet on yourself by striving for better roles and opportunities.

    Job-hopping for money alone isn’t wrong, especially early on. But the returns shrink after the first few hops, and the stress of chasing a slightly bigger paycheck every year will wear you down.

    There’s another career risk with rewards that compound: Seeking positions to work alongside the strongest engineers.

    You might struggle to keep up. You might even get laid off. But the skills you absorb working alongside people better than you are the ones that create durable stability. You build marketable expertise, you see how different organizations actually operate, and every project becomes another tool you carry to the next opportunity. Working next to stronger engineers is a proven way to increase your own expertise.

    If that feels too big, try volunteering for a project you have no idea how to do. The risk is that you fail in front of people. The reward is a new skill and a resume line that opens the next door.

    Compare that with the “safe” path.

    You stay at one company, assuming loyalty will be rewarded. It usually isn’t. And when you finally leave, by choice or not, you may find the skills you built are worth little on the open market. You might be the in-house expert in an aging tech stack while employers are hiring for more cutting edge technologies. Suddenly you’re competing against people with half your experience.

    You could be taking on a risk you didn’t notice.

    Risk Your Time

    The second form is risking your time, which means betting on trends.

    Some trends are non-negotiable. If you’re a software engineer, then cloud services, ReactJS, and AI are mainstream enough that ignoring them actively damages your career. A backend engineer who refuses to learn cloud architecture is volunteering for obsolescence.

    The real gamble is with the smaller trends: the niche tools you stumble onto and find quietly interesting, with no idea whether they’ll matter.

    About two and a half years ago, I learned about retrieval-augmented generation (RAG). Almost no one in my circle was talking about vector databases, a central piece of RAG. Today RAG is close to mainstream, and for once, I had the early-adopter advantage.

    Most of these bets don’t pay off. But when one turns into a major trend, you’re already on the ground floor. Right now I’m making the same bet on voice AI. It isn’t mainstream. It may never be. But if it becomes the next thing, I’m already there, building a foundation.

    Short-Term Risk, Long-Term Stability

    Counter-intuitively, job-hopping and betting on trends gave me the thing I was after the whole time: stability. I’ve rarely struggled to find work, because every risky move stacked skills the market actually wanted.

    If you feel stable and comfortable right now, enjoy it. But ask yourself whether you’re still learning. Because if you’re not, the comfortable choice and the dangerous one may have converged.

    The goal isn’t to avoid the open market forever. It’s to make sure that when you land on it, you’re not at its mercy.

    By Brian Jenney

    P.S. Don’t forget to submit questions about your career or an upcoming decision that you want advice about here!

    —Brian

    What It Means to Be a Mathematician When AI Does the Math

    Until recently, human mathematicians have been central to creating new proofs, even when the work relies on massive computational resources. AI is now challenging that status quo. Writer Benjamin Skuse surveys the ongoing debate in the field about the role of AI, and the existential questions mathematicians have about their own careers. If AI mathematicians surpass human knowledge, could these researchers become “priests to oracles”?

    Read more here.

    Chip R&D Is Accelerating to Keep Pace with AI

    A new partnership between UCLA and five major semiconductor companies is the latest program aiming to bridge the gap between industry and academia. The US $125 million university-industry hub is meant to strengthen collaboration and speed up the R&D process to help meet AI’s fast-paced hardware demands.

    Read more here.

    Why Mentorship Is the Most Underrated Leadership Skill

    True mentorship is far more than friendly advice. This key leadership skill requires advocacy and honest feedback via lasting relationships, and it can strongly benefit both mentor and mentee. Parul Jain, a product management leader at Deloitte, shares what she learned from serving as a mentor—something she didn’t have for much of her own early career.

    Read more here.

19. The AI Arms Race in Technical Interviews Is Escalating

    

    Software engineering jobs are under threat from artificial intelligence. Some applicants are fighting back by using AI in the interview process, employing AI assistants that suggest responses on the fly during remote technical interviews.

    Meanwhile, some employers are countering with—you guessed it—AI. They’re applying AI-powered tools to detect telltale signs of AI use during interviews.

    This two-sided dynamic is turning hiring into an AI arms race with no clear winners. Yet as interviewers and interviewees navigate this daunting reality, experts believe the human aspect of the job search will prevail.

    What’s driving the increase of AI in hiring?

    AI hiring strategist Tatiana Teppoeva characterizes this phenomenon as playing cat and mouse in a climate of relentless AI-fueled tech layoffs and a job market filled with more applicants than open positions.

    “What AI tools do well is identify if a person is performing according to some pattern or expected outcome,” Teppoeva says. When candidates experience constant rejection because they don’t fit the pattern, they might be forced to game the system using AI interview assistants, she adds.

    Archie Payne, co-founder and president at technical recruiting firm CalTek Staffing, views it as a rational response to what he describes as a frustrating process from both sides. “Companies started to use AI resume screeners and similar tools to filter applications at scale. Candidates noticed this and started using AI in their interviews as a countermeasure to what they feel is a process that’s been automated against them,” he says.

    This can lead to an AI-versus-AI loop, according to Ravi Kiran Pagidi, a senior AI data engineer at Navy Federal Credit Union who has been part of technical interview panels for software and data engineering positions. “The process may become less about actual capability and more about who can optimize better for the algorithm,” he says.

    Tools of the trade

    During technical interviews, software engineers might be tasked with outlining algorithms and answering questions related to system design and other software development fundamentals. Remote technical interviews usually turn into live programming sessions, with candidates writing code to solve a specific problem.

    AI interview assistants such as Final Round AI, Interview Coder, and ParakeetAI can listen in, process the audio, and generate answers or code almost instantly. These tools can even be overlaid on the interview screen itself, claiming to appear invisible and undetectable.

    “You’re able to read off an answer that’s coming to you in real time, so all you have to do is put on a little performance,” says Mudit Saraf, a software engineer at Meta.

    Saraf and Shraddha Sunil, a software engineer at Microsoft, cofounded Ginger, an AI voice recruiter for first-round interviews. Ginger asks predefined questions and follow-up queries generated in real time, and it flags candidates who use AI during initial screening calls. The software tracks signals that include eye movement, a consistent delay in response times, tab switching, and speech patterns (phrases or sentence structures and flows) that “sound” like AI.

    Sunil notes that Ginger has been tested mostly for entry-level roles for which applicants might be recent graduates or have only a few years of experience. “These candidates are more used to AI, and they use it a lot, so it’s nothing new to them,” she says.

    Where AI hiring tools fall short

    More employers are deploying AI-assisted interviewing platforms, Payne has noticed, with some seeing mixed results when it comes to AI detection. “The accuracy isn’t perfect yet in the platforms I’ve seen, and there have been a few times strong candidates were flagged as false positives,” he says. “That can be a serious problem when it can already be a challenge to find people qualified for the position without eliminating top performers for no reason.”

    Teppoeva warns of other risks AI interviewing tools could pose, including privacy and security of applicant data, whether interview recordings will be used to train the models underpinning these tools, and bias and fairness.

    A recent study from the Stanford Institute for Human-Centered AI, for instance, found that AI hiring tools can increase racial bias and give rise to systemic rejection. Following 3.4 million real job applicants, whose applications were all assessed by algorithms from a single vendor, the study found evidence of adverse impact for Asian and Black applicants.

    These pitfalls highlight the need for human oversight. “I would definitely incorporate a human somewhere in the process and let humans have a say to make sure the results are fair,” Teppoeva says.

    Audits, clear policies, and transparency are also a must for AI hiring tools, according to Pagidi. “Otherwise, qualified candidates may be filtered out unfairly, and companies may think they are improving efficiency while actually weakening the hiring signal,” he says.

    Reasoning and authenticity go a long way

    Instead of implementing AI detection tools, some tech companies including Meta are allowing AI use during technical interviews. AI-native software development platform Factory is treading the same path.

    “We want our interview process to reflect how candidates actually do their jobs today using AI,” says Varin Nair, a software engineer who leads Factory’s technical hiring process. Applicants build a production-quality system or migrate a real codebase from one framework to another within an hour using AI coding agents. They’re then evaluated based on strategy rather than results.

    “We explicitly do not grade on how many tests pass or whether they finished. We grade on planning, how they direct the AI, how they debug, and whether they can explain why their solution works,” Nair says.

    He’s seen candidates surrender to an AI coding tool, accepting everything it returns. “AI is only as good as the judgment of the person using it,” Nair says. “Weak candidates lean on it to do their thinking and stall the moment it falls short, while strong candidates use it to move faster and free themselves to reason about architecture, trade-offs, and product.”

    Such reasoning remains vital in software development. “Reasoning through edge cases and connecting the answer to production scenarios is where real engineering judgment shows up,” Pagidi says. “Developers will increasingly use AI tools, but they still need to own the final solution.”

    CalTek’s Payne believes this approach of designing interviews to favor authenticity could benefit companies in the long run. “The best technical assessments I’ve seen lately are collaborative, involving codebase walk-throughs and architecture discussions in addition to coding,” he says. “It’s much harder to use AI to get through this kind of interview, so it’s a process that’s more likely to reveal how candidates really think.”

    He also advises candidates to use AI to prepare but to keep answers their own during interviews. “Companies are getting better at detecting AI use, and getting caught can impact your long-term career prospects,” Payne says. “Technical communities are smaller than people think.” With each interview, applicants must weigh the risk and benefit of using these tools. Taking that risk, he says, rarely works in the candidate’s favor.

20. IEEE Remembers Pioneering Computer Scientist Peter G. Neumann

    

    The computing community recently lost one of its enduring voices: IEEE Fellow Peter G. Neumann. The renowned computer scientist and respected risk analyst died on 17 May at the age of 93.

    For almost 70 years, Neumann shaped the computing field through his pioneering work on risks, system dependability, security, and fault tolerance with rare intellectual depth and unwavering ethical clarity.

    Five of those decades were spent as a principal scientist at SRI International in Menlo Park, Calif., where he worked until his death. A detailed narrative of his work, life, and mentoring is available on his SRI web page, where he chronicled his journey.

    He possessed a rare ability to identify systemic vulnerabilities long before they became widely recognized. He cautioned that interconnected systems, if poorly designed or insufficiently scrutinized, could fail and become targets for exploitation. He insisted innovation always must be accompanied by responsibility, reliability, and a clear understanding of the risks involved.

    With the widespread adoption of computing, information technology, artificial intelligence, and autonomous systems, Neumann’s insights have become more relevant.

    From Harvard to Bell Labs

    Neumann was born on 21 September 1932 in New York City. After graduating from high school, he pursued a degree in mathematics at Harvard, where he had a conversation that shaped his approach to research, according to the Association for Computing Machinery (ACM). In November 1952 he had a two-hour breakfast meeting with Albert Einstein, at which they discussed the importance of simplicity in design.

    Neumann was among the first generation of Harvard students to program computers and, remarkably for that era, enjoyed exclusive access to the computing systems.

    After earning his bachelor’s degree in 1954, he continued his education at Harvard, earning a master’s degree in 1955. In 1958 he moved to Germany to become a doctoral student at the Technical University of Darmstadt as part of the Fulbright program, which provides funding for U.S. citizens to study or teach abroad. He earned his doctorate in 1960.

    After returning to the United States, he joined Bell Labs in Murray Hill, N.J., where he worked on error-correcting codes and survivable communications. He also pursued a second Ph.D. in applied mathematics and science at Harvard, achieving that goal in 1961.

    Four years later, he was assigned to work on Multics, which became an influential operating system that shaped modern secure computing architectures. Multics was a mainframe time-sharing system designed to serve the diverse needs of multiple users simultaneously. Neumann designed its filing system, which featured hierarchical directories, access control lists, and dynamically paged virtual memory segments. He also played a key role in the design of its input/output system.

    In 1970 he left Bell Labs to join SRI.

    Technical contributions at SRI

    Neumann made several seminal and foundational technical contributions while at SRI, including the following:

    • Provably Secure Operating System. The PSOS project he worked on advanced formal methods in operating systems and computer security. The project demonstrated that security could be designed within the initial plan rather than retrofitted.
    • Election integrity and voting systems. He outlined vulnerabilities in electronic systems and advocated for transparency, verifiability, and public accountability.
    • Systems-level risk thinking. He broadened the concept of computer security to encompass human factors, governance, policy failures, social consequences, organizational negligence, and misuse of automation. His system-level perspective now fuels debates on AI governance and digital trust.
    • Intrusion-detection systems. With his colleague Dorothy E. Denning, a security expert, he helped develop an intrusion-detection expert system (IDES), laying the groundwork for modern cyberdefenses.
    • CHERI. He promoted hardware-assisted secure computing: technology that now influences next-generation processors. TheCapability Hardware-Enhanced RISC Instructions (CHERI) architecture project, which Neumann led, is now being commercialized by an international, nonprofit alliance.

    His contributions are united by a simple but profound principle: Security should be foundational, not incidental. Neumann argued that security must be embedded into system architecture from the start—not patched after deployment.

    ACM’s Risks Forum

    Neumann’s other enduring contribution was the creation and stewardship of the ACM Risks Forum, formally known as the Forum on Risks to the Public in Computers and Related Systems. For decades, it was one of the most respected online arenas for critical reflection on computing failures, vulnerabilities, security breaches, unintended consequences, and emerging technological threats. He transformed the forum into a scholarly archive of cautionary lessons in computing failures and risks.

    In 1985 he started documenting how technological systems fail when complexity exceeds understanding and when society places blind trust in automation. He then moderated the community for 41 years, leaving his position in April, weeks before his passing.

    In 1995 he published Computer-Related Risks, a book that serves as a case-driven guide to how computer systems fail and why. It is still relevant in an era defined by AI, growing cyberthreats, and our deep digital dependence.

    Intellectual rigor with grace and humility

    Neumann viewed computing not as an abstract technical pursuit but as a profoundly human enterprise carrying societal responsibilities. He was thoughtfully skeptical, questioned assumptions, and challenged complacency. His observations often anticipated challenges years before they became mainstream concerns.

    He exemplified high scholarship ideals and was intellectually honest and ethically steadfast. He had been a frequent critic of lax attitudes the industry has maintained toward both computer security and individual digital privacy. He warned against the industry’s tendency to repeat mistakes.

    Neumann’s signature contribution was not technical but a stance. He insisted, against industry custom, that recurring computer failures were not unfortunate accidents but rather were predictable consequences of how systems were built and sold.

    He was fundamentally an optimist about what can be done with research and was a pessimist about corporations.

    Security is not merely a technical patch, he said, but is a systemic property requiring sound design, governance, and human judgment. He consistently warned that uncontrolled complexity is itself a source of risk.

    His signature contribution was not technical but a stance. He insisted, against industry custom, that recurring computer failures were not unfortunate accidents but rather were predictable consequences of how systems were built and sold.

    Honors and recognitions

    Neumann was honored with a number of honors including the Electronic Privacy Information Center’s 2018 Lifetime Achievement Award, the Computing Research Association’s 2013 Distinguished Service Award, and ACM’s 2005 Special Interest Group on Security, Audit, and Control Outstanding Contributions Award.

    In addition to being an IEEE Fellow, he was a Fellow of ACM, the American Association for the Advancement of Science, and SRI. In 2012 he was inducted into the Cyber Security Hall of Fame.

    An enduring legacy

    Neumann’s greatest legacy is not necessarily his inventions but his way of thinking. His longtime interest was the risk ecology of computing—the business, technological, social, political, and personal risks that computing has created, along with its tremendous benefits in each of those spheres. He left us a timely lesson: Innovation must be accompanied by responsibility, foresight, and care.

    Neumann was “one of the last of the old guard and a pointer to the future,” observed IEEE Life Fellow Whitfield Diffie, who helped invent public key cryptography. Highlighting both the significance and enduring relevance of Neumann’s work, a tribute by blogger Phoenix AMTD aptly said: “He spent 70 years cataloging how computers fail. We spent 70 years not listening. Maybe now we will.”

    Let’s honor Peter G. Neumann not merely by remembering his advice but by following it.

21. STEM Needs Leaders From Every Generation at the Table

    

    Working in isolation, especially for leaders, is rapidly becoming an outmoded idea. The modern era is defined by rapid technological advancements and increasingly complex, collaborative global challenges. In this environment, leadership can no longer be approached as an individual pursuit.

    Instead, leadership must be a collaborative effort in which knowledge, responsibility, and innovation are continuously exchanged across teams, roles, and areas of expertise. Success depends on the ability to foster connection, leverage diverse perspectives, and work collectively toward shared outcomes.

    The shift is especially important in science, technology, engineering, and mathematics fields.

    IEEE is bringing together emerging professionals and established experts and leaders at the inaugural IEEE International Leadership Conference to address the need for cross-generational knowledge-sharing and to equip professionals with tools for collaborative leadership. Honoring Expertise, Accelerating Potential is the theme of the ILC, scheduled for 3 and 4 October in Budapest.

    The conference is expected to focus on how leaders can share information across roles, adapt to rapid technological advancements, and build stronger, more connected professional communities. Through discussions, panels, and interactive sessions, attendees can examine how collaboration across experience levels and disciplines can strengthen decision-making and foment innovation.

    “There are several factors driving this shift [in leadership], including accelerating technological development cycles, the need to build public trust, and the large percentage of the STEM workforce approaching retirement,” says Vickie Ozburn, conference cochair. “Progress in STEM now depends less on individual brilliance and more on the ability to transfer knowledge, adapt, and make decisions that integrate technical expertise with ethical and social considerations.”

    From hierarchies to shared leadership

    Instead of traditional corporate models rooted in hierarchy and individual advancement, a more dynamic framework is taking shape, one that views leadership as a shared ecosystem built on mentorship, continuous learning, and intentional knowledge transfer.

    It means recognizing that professional development is no longer a one-directional flow of experience from senior professionals to newcomers. Instead, it thrives as a multidirectional exchange. When emerging professionals, mid-career managers, and seasoned experts including retirees are brought together, the result is not only richer dialogue but also more resilient and well-informed decision-making. A cross-generational dialogue enables organizations to honor what has worked, critically assess what has failed, and thoughtfully shape what needs to evolve.

    Bridging experience to drive future leadership

    Howard Wolfman, cochair of the IEEE ILC, underscores the importance of historical perspective in leadership development, invoking George Santayana’s enduring insight: “Those who cannot remember the past are condemned to repeat it.”

    “In STEM especially, this principle carries significant weight,” says Wolfman, an IEEE life senior member and the founder and principal of Lumispec Consulting, in Northbrook, Ill. “Technological innovation doesn’t happen all of a sudden; it builds on decades of research, lessons learned, and accumulated knowledge. When leaders actively connect insights from across experience levels, they gain a more complete understanding of both opportunity and risk.”

    That perspective reinforces the need for greater collaboration across roles and experience levels, ensuring that knowledge is not lost and is continuously built upon and applied in new ways. In this way, leadership development becomes a continuous, interconnected process rather than a series of isolated stages.

    STEM careers are no longer defined by linear progression but by evolving contributions, in which each phase adds value to the field’s broader advancement.

    What the changes mean for leaders today

    Adopting a new leadership paradigm requires a shift in mindset across all levels. For senior leaders, success is defined not only by what they have built but also by the people they mentor and the knowledge they pass forward. Their legacy lies in enabling future leaders to succeed.

    For emerging young professionals, innovation becomes more informed and impactful when it is grounded in historical context and informed by those who have already navigated similar challenges.

    “Technological innovation doesn’t happen all of a sudden; it builds on decades of research, lessons learned, and accumulated knowledge. When leaders actively connect insights from across experience levels, they gain a more complete understanding of both opportunity and risk.”—Howard Wolfman, cochair of the IEEE International Leadership Conference

    For organizations, cross-generational collaboration should be recognized as a strategic advantage, not merely an aspiration. Creating environments where knowledge flows freely and diverse perspectives are actively integrated is essential for long-term success.

    The evolution reframes the distinction between management and leadership.

    “A leader does the right thing, and a manager does things right,” Wolfman says. As the environment continues to shift, doing the right thing increasingly depends on drawing insights from across generations and experiences.

    Building future-ready leadership pipelines

    To build leadership pipelines capable of sustaining innovation and trust, organizations must begin asking more intentional questions:

    • How do we create systems where knowledge sharing is continuous rather than episodic?
    • How do we elevate emerging voices earlier in their careers?
    • How do we ensure that experienced professionals remain engaged and valued contributors?
    • How do we design leadership development as a collaborative, inclusive process rather than a competitive one?

    Ultimately, leadership cannot be tied solely to titles or tenure. It is about contributing to a continuum in which each generation strengthens the next.

    The IEEE ILC attendees are likely to leave the event with new insights and with a transformed perspective: Leadership is not about waiting for advancement or recognition; it is about engaging in an exchange of knowledge, responsibility, and vision, where the strength of the whole depends on the contributions of every generation.

    Registration for the conference opens soon.

22. IEEE Honors Robotics Pioneer Toshio Fukuda

    

    Toshio Fukuda has been blazing trails for most of his career. He is considered to be one of the most prolific scholars in robotics, writing more than 2,000 research papers and authoring several books on the field. He’s an influential figure thanks to his pioneering work developing biomedical robotic systems, industrial robots, micro-nano robotics, mechatronics, and AI-driven automation.

    Fukuda launched one of the first robotics conferences, the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). It is still popular almost 40 years later.

    Toshio Fukuda

    Employer

    Egypt-Japan University of Science and Technology, in Alexandria

    Title

    Professor and vice president of research

    Member grade

    Life Fellow

    Alma maters

    Waseda University, in Tokyo; University of Tokyo

    An IEEE Life Fellow, he is a professor emeritus in the department of micro-nano systems engineering and a visiting professor at Nagoya University, in Japan, where he taught for nearly 25 years. Currently, he is a vice president of research at the Egypt-Japan University of Science and Technology, in Alexandria, Egypt.

    Within IEEE, Fukuda has held top volunteer positions including the organization’s highest office: He served as IEEE president in 2020, becoming the first person of Asian descent to hold the role.

    He’s a former program director of Japan’s Moonshot program, which by 2050 intends to develop advanced AI robots.

    Born in Japan, Fukuda has been recognized by the country for his contributions to science with two of its highest awards: the Medal of Honor with a purple ribbon in 2015 and the Order of the Sacred Treasure in 2022.

    IEEE honored him with this year’s Richard M. Emberson Award for “distinguished service advancing the technical objectives of IEEE, especially in the area of robotics.” The IEEE Board-level award is sponsored by the IEEE Technical Activities Board. Fukuda received the award on 24 April at a ceremony in New York City.

    As a former IEEE president who has served as a master of ceremonies at several of the organization’s major award events, Fukuda noted that he is more accustomed to bestowing awards than receiving them.

    “It’s very interesting to be on the receiving end,” he says.

    The journey into robotics research

    As a teenager, Fukuda spent his summer breaks teaching himself how to build things including transistor radios and steam engines.

    “It was very nice to have a hands-on hobby and make these kinds of things myself,” he says. His experimentation led him to study engineering.

    He earned a bachelor’s degree in engineering in 1971 from Waseda University, in Tokyo. He says one of his professors there—Ichiro Kato, regarded as the father of Japanese robotics research—was a good mentor who made a positive impact.

    Fukuda’s research interests were robotics and mechatronics, a field that combines robotics, electronics, computer science, and control systems.

    He went on to earn a master’s degree and a doctorate in science from the University of Tokyo, in 1971 and 1977. During those years, he also attended Yale, where he conducted research on advanced control theory in 1973.

    He reflects fondly on his time at Yale: “It was a very nice environment and a kind of free-thinking atmosphere. It motivated me to study more.”

    “IEEE doesn’t care who you are, what you do, what country you are from, or whether you are male or female. IEEE accepts people who have energy and passion.”

    While at Yale, Fukuda served as an assistant to his advisor—which led him to consider a career in academia, he says, because he enjoyed the freedom that research work afforded him.

    But he realized that such freedom comes with a price. University researchers are expected to raise the money that funds their work. He compares researchers to small-business owners who have to bring in money to keep their enterprise afloat.

    That realization led him to select robotics as his field because he intended to develop technologies useful to industry, he says.

    After earning his doctorate, he returned to Japan in 1977 to work as a research scientist at the government’s Mechanical Engineering Laboratory, later renamed the National Institute of Advanced Industrial Science and Technology, in Tsukuba.

    “There was a lot of research going on at the lab, including practical robotics and theory,” he says.

    He left Japan in 1979 to become a visiting research fellow at the University of Stuttgart, in Germany. During his year there, he studied systems, software problems, and related topics.

    He returned to Japan and was hired as an associate professor of mechanical engineering at the Tokyo University of Science. He conducted research into practical uses for robots by visiting industrial plants. He decided to develop robots that inspect industrial equipment such as those used in assembly plants, oil refineries, and power stations—places that “can be hostile environments for humans,” he says.

    His work drew interest from chemical, oil, and utility companies.

    “I got a lot of money from them for this very practical application, which funded my research,” he says, laughing.

    Developing popular robotic systems

    Fukuda grew tired of making those robots, he says, so he switched to creating ones for scientific applications. He developed many techniques, but he probably is best known for his modular, cellular robotic systems (CEBOTs), which he introduced in 1985.

    He has described how CEBOTs work in numerous papers published in the IEEE Xplore Digital Library.

    The CEBOT system is composed of a number of autonomous robotic cells that stick together like interlocking Lego plastic bricks, he says.

    Each cell is a fundamental modular unit that has a function. When a simple task is given, the system can analyze it and generate the structure of the cellular manipulator. The cells connect to and detach from each other through connection mechanisms and cooperate mutually, creating complex structures and configurations.

    “You start developing from the component-wise to the cell-wise to a small functional unit—and then you come up with clusters that make bigger systems. We can make a society of robot beings like that,” he explained in his oral history published on the Engineering and Technology History Wiki. “It’s a distributed robotic system, a self-organized robotic system, and also an evolutionary robotic system.

    “It’s also a fault-tolerant robot system because if something is wrong, you just remove those things and make a new one. You keep the system working. That’s a great thing.”

    Today CEBOTs are used for a variety of tasks such as delivering medication in hospitals, assisting with planting crops, and transporting products in distribution centers. Check out IEEE Spectrum’s Robots Guide for news from the world of robotics.

    In 1989 Fukuda joined Nagoya University as a professor of mechanical engineering and micro-nano systems engineering. During his 24-year career there, he was director of the university’s Center for Micro-Nano Mechatronics. He developed a long list of technologies at the university, including many for medical applications. He also conducted groundbreaking research into intelligent robotic systems and micro- and nano-robotics.

    Another technology he is known for is brachiation robots, which he helped develop in 1988. He calls them monkey robots because they’re based on the pendulum-like movement of monkeys swinging from tree to tree. The gravity-based locomotion enables continuous movement.

    Brachiation robots now are inspecting high-voltage transmission towers and bridges, searching damaged buildings for survivors, and performing maintenance on pipelines and cables.

    Fukuda retired from the university in 2013 and was named professor emeritus.

    He didn’t stay retired for long, though. He next held a teaching appointment at Meijo University, in Nagoya, until he left in 2022 to join the Egypt-Japan University.

    A prominent volunteer

    He joined IEEE in 1980 at the encouragement of one of his research advisors, Professor Fumio Harashima, now an IEEE Life Fellow. After attending conferences and reading the organization’s publications, Fukuda says, he looked forward to becoming more involved.

    “I wanted to know how to organize a conference and how to edit a paper for one of its Transactions,” he says. “I wanted to know what was going on from inside the organization, not just the outside.”

    In 1988 he was the founding chair and organizer of IROS, in Tokyo. The conference had 330 attendees that year, and was supported by Harashima. Today it is one of the largest and most prestigious conferences on the topic, attracting more than 9,000 people annually. Out of 120,000 conferences, it was the only conference in the Nature Index database for this year, Fukuda says.

    In 1996 he and other members launched IEEE Transactions on Mechatronics.

    He was the founding president of the IEEE Nanotechnology Council, which was established in 2002. He is considered a pioneer in nanotechnology research, particularly regarding how it relates to robotics.

    Over the years, he has held numerous volunteer positions on IEEE editorial boards and committees.

    He was the 1998–1999 president of the IEEE Robotics and Automation Society, becoming the first non-U.S. member to hold the title.

    He was director of IEEE Division X (2001–2002 and 2017–2018), which covers intelligent systems, biological engineering, robotics, control systems, and photonic technologies. He served as the 2013–2014 director of IEEE Region 10 (Asia-Pacific).

    As the 2020 IEEE president, Fukuda saw the organization through the early part of the COVID-19 pandemic. Because of travel restrictions, he realized IEEE should change how it offered its in-person services, specifically educational programs. He encouraged IEEE Educational Activities to develop an online learning platform. The IEEE Learning Network started with just three courses and now offers nearly 2,000 courses, webinars, and learning materials.

    An award-winning member

    The Emberson Award joins a slew of other recognitions Fukuda has received from IEEE. They include several from the IEEE Robotics and Automation Society: a 2004 Pioneer Award, a 2009 Saridis Leadership Award, and the 2011 Harashima Award for Innovative Technologies. He is also a recipient of the Board-level 2010 IEEE Robotics and Automation Technical Field Award.

    He says he feels strongly that IEEE should be a diverse organization that is welcoming to all. As IEEE president, he led efforts to devise a diversity, equity, and inclusion program. Several policies, procedures, and bylaws were revised to give members a safe, inclusive place for discourse.

    “It’s important for IEEE to make everyone feel comfortable,” he says. “DEI programs are important. All people should be equal. IEEE doesn’t care who you are, what you do, what country you are from, or whether you are male or female. IEEE accepts people who have energy and passion.

    “It accepted me, from the Far East. That’s why I like it.”

    You can learn more about Fukuda and his career from the oral history conducted by the IEEE History Center.

23. Why Public Speaking Skills Are Worth Investing In

    

    This article is crossposted fromIEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Parsity anddelivered to your inbox for free!

    You want to become a senior developer. A CTO, maybe. Start your own company, perhaps. Or maybe you just want to land your first role in tech.

    You will not get there from raw engineering skill alone.

    There’s a skill that’s quietly essential to technical leadership and yet consistently overlooked: public speaking.

    If you’re anything like I used to be, you’re already listing reasons not to. “I got into this to code, not to give presentations.” “I don’t want to lead.” “I’m too junior to speak about anything.” No, no, and no again. There’s a ceiling on the return from technical skill alone.

    I was terrified of public speaking for the first three years of my career. I wanted to hide behind code, and for the most part it worked. I did my job and did it well.

    Then I joined a startup where hiding wasn’t an option. The whole company was five people. I was one of two developers. I had to form opinions on our technical direction and defend them, and the CTO told me directly that I needed to speak up more.

    A few things happened once I did. I took more pride in my work. I said some cringe-worthy stuff, lived through the mini-anxiety attacks, and got better. To my own disbelief, I’m now an engineering manager whose job is largely speaking to groups of developers and leading presentations, online and in person.

    Here’s why this is worth your time:

    Leadership. Communicating ideas clearly, influencing decisions, and aligning your team are core leadership functions, and they matter more the further you climb.

    Visibility. Speaking lets you show your expertise, build a reputation, and connect with people who open doors to better roles.

    Durability. As automation absorbs more routine technical work, skills rooted in human interaction and judgment are far harder to replace.

    The good news is you can build this deliberately, in low-stakes steps.

    Record yourself. Use a screen-recording tool to walk through your work, explain a concept, or narrate your code. You can edit, re-record, and over-think it as much as you want. That’s the point. It gets you comfortable on camera before the stakes are real.

    Volunteer for demos. Next time you ship a feature or fix a bug, ask your manager for a short time slot to walk the team through it. No format for that on your team? Suggest a monthly lunch-and-learn and kick it off with a 15-minute lightning talk on something you know.

    Start small—really small. If your anxiety is spiking, don’t jump into the deep end. In your next meeting, ask one question. Write it down beforehand if you have to. Then be the first to break the awkward silence when someone else asks one. Developers are a famously quiet bunch, so it doesn’t take much to stand out.

    The further you grow, the more you’ll be expected to hold opinions and voice them publicly. So start now. Record yourself, ask questions, get uncomfortable, and notice that it gets easier every time you do it.

    —Brian

    War Taught this Ukrainian Entrepreneur the Value of Resilience

    Salome Mikadze-Struk built her tech company Movadex as an undergraduate student at the height of the COVID-19 pandemic—then kept it running during the outbreak of war in her native Ukraine. Now, she’s channeling what she learned into mentoring tech founders and speaking about the importance of resilience as AI upends the software industry.

    Read more here.

    IEEE Rolls Out Large Language Models Virtual Training Course

    LLMs are now part of many engineers’ daily workflow, and the demand for technical expertise in implementing and securing the models is rising. But to build tools that work consistently, developers must have a strong understanding of the core principles that govern how the models work. IEEE is now offering a five-course program to teach how to use LLMs effectively, starting with the fundamental engineering behind the technology.

    Read more here.

    Make an Origami Circuit Board

    Two researchers at the City University of Hong Kong developed a method to make a circuit trace by simply bending a piece of paperlike material. With the right ingredients—isopropanol and liquid metal—you can make your own origami circuit board. The researchers also created a toolkit, called LiqMetCraft, with software tools and instructions to make it easy for beginners, whether in papercraft or electronics.

    Read more here.

24. Why Mentorship Is the Most Underrated Leadership Skill

    

    I started my professional journey as an engineer before moving into product strategy and innovation leadership roles for several global technology organizations. Over the years, I have served as a mentor for a variety of programs including Products That Count’s strategic product management, Women in Product mentorship initiatives, and Alchemist accelerator programs.

    In 2024 and 2025 I led Walmart’s Women in Product mentorship program. I was responsible for designing and implementing the programs, including managing participant registration, matching mentors with mentees, and establishing clear standards for how they would interact.

    Yet for much of my own early career, I never really had a mentor.

    As an individual contributor engineer, I was focused on solving problems, delivering results, and figuring things out independently. I was hesitant to ask for help for fear of being judged for what I didn’t know.

    Part of that was also temperament. I am naturally introverted.

    That mindset rewarded me well. It made me self-reliant, resilient, and deeply driven. But it also had limits. Looking back, I now realize that believing I had to navigate everything alone was not always a strength. I sometimes wonder how many opportunities I missed simply because I never asked for help.

    As I moved into product management and later strategy roles, I began collaborating with larger teams, departments, and organizations. The work itself became more cross-functional and people-centered. Over time, I started recognizing the value of mentorship, sponsorship, and collaborative growth in ways I had not appreciated earlier in my career.

    I received valuable advice from different people at important moments throughout my career. Some helped me navigate conflict with more clarity. Others helped me communicate my contributions more effectively. And others gave me perspective on how to approach uncertainty, deal with organizational complexity, and avoid burnout.

    But those moments were not the same as mentorship. They were valuable but infrequent interactions, not sustained relationships. No one consistently guided me through difficult decisions, advocated for me with decision-makers and senior leadership, or actively invested in my long-term growth.

    My understanding of mentorship changed not as a mentee but as a mentor.

    A leadership multiplier

    Mentorship is often seen as an act of goodwill: admirable but optional. In reality, effective mentorship can be a competitive advantage for everyone involved.

    For mentees, it can accelerate career growth, strengthen decision-making, and create access to opportunities that hard work alone does not always unlock.

    Mentorship strengthens an individual’s leadership skills, empathy, and the ability to develop future talent.

    For organizations, mentorship builds stronger leadership pipelines, more resilient teams, and healthier cultures of growth and trust.

    By getting involved, I began to understand that meaningful mentorship is not simply occasional advice or career guidance. At its best, it is an active investment in another person’s growth. It includes advocacy, sponsorship, honest feedback, visibility, and sometimes helping people access opportunities they may not have reached on their own.

    That is why mentorship should not be treated as kindness or incidental support. It is one of the most practical, hands-on, and personal forms of leadership.

    Advocacy changes careers

    Advice can help someone improve, but advocacy and sponsorship can change the direction of a career.

    In many organizations, career growth depends not only on talent but also on access to honest feedback, influential networks, and sponsors willing to speak about someone’s potential when opportunities are discussed. Access also includes introductions to people who can recognize the value and impact of a person’s work.

    Sometimes the difference between advice and true sponsorship is illustrated more clearly through stories rather than through leadership frameworks. In The Devil Wears Prada and its sequel Nigel’s relationship with Andy evolves far beyond workplace advice. In the 2006 movie, he helps her grow professionally, pushes her to envision a more expansive future, and guides her through an unfamiliar industry.

    In the sequel—set two decades later—his investment in her success continues even though their careers diverge. When Andy (played by Anne Hathaway) is laid off during a difficult job market and struggles to find meaningful opportunities, Nigel (Stanley Tucci) quietly recommends her for a role at his firm. She is arguably overqualified for the position, but Nigel recognizes that it is the right opportunity at the right time. His recommendation helps her transition from a career in the news back into working in fashion. She can regain stability and ultimately rebuild career momentum. Over time, the opportunity becomes a turning point, reshaping her professional trajectory.

    What makes it meaningful is not just the recommendation itself. It is that Nigel continued paying attention to her career growth over the years, believed in her potential, and supported her when she needed it.

    That is what meaningful mentorship and sponsorship often look like in practice: not surface-level guidance but genuine investment in someone’s long-term growth and success.

    When mentors provide that kind of support intentionally, mentorship becomes more than guidance. It becomes a competitive advantage—not only for the mentee but also for the mentor and the organization.

    Why inclusive mentorship matters

    Mentorship matters because talent alone does not shape a career. Access is important. In many workplaces, advancement depends not only on capability but on guidance, sponsorship, visibility, and informal knowledge about upcoming job opportunities.

    Not everyone has equal access to such advantages. Research from McKinsey and Lean In suggests that women often receive less mentorship, sponsorship, and career support than men do, even in organizations that publicly emphasize inclusion and leadership development.

    When mentorship is left entirely to informal networks, opportunity often becomes uneven. And when it’s left to chance, opportunity also is uneven.

    That’s why inclusive mentorship matters. It creates a more intentional way to support people who might otherwise be overlooked.

    What great mentors require

    “A mentor is someone who allows you to see the hope inside yourself,” Oprah Winfrey once said.

    Great mentorship is not about having all the answers. It’s about showing up with intention. It means listening closely, being candid, and helping someone grow with more confidence and clarity.

    The best mentors respect their mentees’ time. They come prepared and listen for what is needed rather than rushing to give advice. They are open about their successes and failures because honesty builds trust faster than polished stories do. Great mentors tailor their guidance to the individual and encourage growth while also creating accountability.

    Above all, good mentors create a psychologically safe space. They make it easier for mentees to ask difficult questions, test or pitch ideas, and talk openly about issues without fear of being judged. Growth usually starts at that point.

    Organizations have a role to play as well. If mentorship matters, the program should be visible and supported.

    That can mean including it in stated expectations of leaders, creating ways to connect mentors and mentees, providing mentorship training, and recognizing outcomes that go beyond performance metrics.

    It also can mean broadening the understanding of mentorship. Peer mentorship, cross-functional mentorship, and even cross-industry mentorship can play important roles.

    The leadership gap many organizations ignore

    Promoting mentorship should not involve forcing artificial relationships or turning an employee’s growth into a line on someone’s to-do list. Organizations ought to promote the idea that leaders should invest in others, helping to build stronger teams, more capable leaders, and more organizational resiliency.

    At a minimum, organizations should ask mentors whether they helped their mentee grow in their career and whether the mentee became more confident, capable, or prepared as a result of the relationship. Did they help junior employees navigate the organization more effectively? What opportunities did they create or find to give the mentees more visibility? Did they help mentees develop communication, leadership, or decision-making skills?

    Those questions might be hard to quantify, but they get close to the substance of leadership.

    Legacy is built through people

    People might remember the strategies a leader shaped, the products the leader created, or the financial targets that were hit. Such accomplishments matter, of course. But another part of leadership lasts longer. It lives in the coworkers whose careers were advanced because someone took the time to invest in them.

25. This Senior Member Solves Complex Product Lifecycle Challenges

    

    What do an instinct to fix things and the 1999 global panic over whether computers would survive the date change to 2000, known as the Y2K bug, have in common? Both helped shape IEEE Senior Member Ajay Prasad’s career.

    Prasad is an industry process director at Dassault Systèmes in Detroit. His focus is global oversight of industry process experts specializing in Enovia, a product lifecycle management (PLM) solution and one of the company’s flagship products.

    Ajay Prasad

    Employer

    Dassault Systèmes in Detroit

    Title

    Industry process director

    Member grade

    Senior member

    Alma maters

    Bangalore University, in Bengaluru, India; and the University of Birmingham, England

    As a child growing up in Bangalore, India, his curiosity to build real-world solutions was ignited by his father, a mechanical engineer. Prasad’s father often fixed things around the house, including cars and bicycles. His ability to take something broken and return it to working order laid the groundwork for his son’s career in engineering.

    Prasad was in his final year of undergraduate studies when the Y2K panic hit its peak.

    “Nobody knew what would happen when the year turned to 2000,” he says, “and it was almost projected like the end of the world was coming.”

    The phenomenon left him with the desire to fix computer problems, but he wasn’t sure how he would go about it, as he had no background in computer science.

    As it turned out, computer systems didn’t crash when the 1900s ended. The world did not end on Jan. 1, 2000, and neither did his interest in how computers worked.

    The consulting pivot that changed his career

    Prasad graduated in 2000 with a bachelor’s degree in industrial engineering and management from the RV College of Engineering, in Bengaluru. It was at a time when tech companies were heavily recruiting engineers, regardless of their specialization.

    “They were mainly looking for problem-solving skills,” Prasad says.

    His parents expected him to immediately enroll in a master’s degree program, he says, but a job offer from Tata Consultancy Services in Bengaluru to work as an assistant systems engineer trainee changed that plan.

    “My dad was actually out of town for work when the job offer came in,” he says. “I knew he wanted me to stay in school, but honestly, I was done studying for a while. I wanted to get some work experience.”

    He accepted the offer, then broke the news to his father. His parents were supportive of his decision, but his dad offered one piece of advice: Keep the idea of an advanced degree in the back of his mind.

    Several months of working on mainframes helped him understand algorithms and how to code to achieve outcomes, he says, and the more he learned about computer systems, the more he wanted to pursue a computer science career. With a solid engineering foundation, he says, he knew the pivot made sense. But he also wanted the academic credentials to back up his tech skills.

    Heeding his father’s advice, he paused his career at Tata and enrolled in the master’s degree program in computer science at the University of Birmingham in England. At the time, it was one of the few schools offering the program to students who had no undergraduate computer science degree. When he graduated in 2002, he briefly considered pursuing a Ph.D., but he returned to India and a new role at Tata.

    Building a global perspective

    As a systems engineer, he worked on the MatrixOne platform, a PLM software solution that helped manufacturers oversee products from design to launch. He spent a lot of time customizing the MatrixOne software to meet customer needs. The experience gave him insights into the pain points that different users of the platform faced, such as managing complex product data across large teams and keeping track of complicated supply chains.

    In 2004 Tata transferred him to Minneapolis, where he continued working on the MatrixOne platform.

    During that time, Dassault acquired MatrixOne and folded it into its existing Enovia product line. He remained involved with the product until he left Tata in 2008. To scratch an entrepreneurial itch, he became a consultant for the product, helping customize the platform for U.S. clients.

    The move also forced him to make a decision: He needed to choose between settling in the United States or returning to India. Inclement weather made up his mind, he says.

    “I was heading to my next project across the country, and it was winter,” he says. “During the entire drive, I was trying, unsuccessfully, to outrun a massive snowstorm. I was young, and it was an adventure, but it helped clarify where I wanted to be at that point in my life.”

    He returned to India in 2010, armed with a more global perspective and expertise with Enovia. As he looked for a job, he focused on a role with the company that owned the platform he’d worked on for years.

    “Dassault Systèmes has continuously pioneered new technologies and concepts and set benchmarks in the PLM space,” he says. “When an opportunity opened up there for me, I jumped at it.”

    Instead of a programming role, though, he was hired as an Enovia technical sales specialist, working in Dassault’s Bengaluru location. It was an eye-opening experience, he says.

    “It put me on the other side of the table: trying to sell software to customers,” he says. “This was the opposite of my experience customizing software after the sale was complete.”

    The role of technical sales

    The position involved both presale and postsale duties. Technical salespeople bring subject-matter expertise that bridges the gap between a product’s functionality and the customer’s needs. The role works directly with the sales team to craft a presentation that showcases the value of the software as a solution.

    On the postsale side, technical sales professionals work with service teams to customize software solutions to ensure customer goals are met. If needed functionality doesn’t exist, they work with the R&D group to create it. They also offer suggestions to customers on how to improve their processes.

    When Prasad stepped into his new role, a senior colleague described technical sales as an “exam syndrome” because customers are judging you and your presentation against competitors. The analogy didn’t land well with him.

    Recalling all his years of formal education, he had a different perspective: “I wanted to think of it more as an opportunity to fully understand a customer’s problem, then solve it better than anybody else could.

    “Every customer has unique pain points. When I can offer solutions that deliver value, they’ll buy the software.”

    It’s his belief that the position is best served by professionals with both engineering and computer science backgrounds. He advocates that engineering students consider adding computer science to their studies, and he draws on his own educational experiences to support the position.

    Combining engineering and computer science

    Dassault recognized the value in his approach. In 2015 he was hand-picked to be part of the company’s new Worldwide Enovia Center of Excellence team in Auburn Hills, Mich. As an industry process expert, he was able to put his Enovia expertise into action.

    He’s now a senior leader managing a global technical sales team. One of his objectives, he says, is advocating to engineers that technical sales is a viable career move.

    “The moment an engineer hears the word sales, they tend to stop listening,” he says. “They don’t want to be a salesperson in the traditional sense.”

    That’s too narrow a view, he says, adding: “I think everyone is a salesperson to some degree.”

    If engineers looked at technical sales differently, they’d see an exciting opportunity, he contends.

    “In this role, they have the ability to not only develop solutions but also explore the why behind the need for a solution at all,” he says.

    “As engineers, sometimes we are so focused on engineering concepts and principles that we get bogged down in the details and don’t focus on what the problem really is,” he says. “I learned with technology that even before you try and create a solution, you need to understand the logic of the problem first.”

    From problems to patents

    His approach has delivered measurable results. He holds one patent and has a second under consideration. His combination of engineering and computer science expertise played a crucial role in each, he says.

    His first patent, granted in 2023 by the U.S. Patent and Trademark Office, was for his solution to improve product benchmarking for clients with large-scale data management issues. It replaces traditional spreadsheets with powerful databases and a user-friendly interface, ensuring information is up to date, accessible, and shareable.

    “I think that being part of the IEEE community is a huge value for folks in the engineering space. It’s a great way to collaborate and to understand what’s happening, especially in your local ecosystem.”

    His second patent, pending with the USPTO, is designed to help customers manage large projects that involve a high volume of engineering design tasks. Instead of relying on ambiguous communication between engineers and project managers, his solution would draw data from the work management system and update the project management dashboard automatically. It would replace guesswork with real-time data.

    Prasad has authored the peer-reviewed technical paper “Transforming Product Development With a Platform-Based Approach to Product Lifecycle Management,” which was published by SAE International. His writings on the use of data tracking and AI in product lifecycle management have appeared on Engineering.com and in Wavelengths, a monthly publication from the IEEE Southeastern Michigan Section.

    In February, Dassault marked Prasad’s success by promoting him to worldwide Enovia industry process director. The title reflects a career built on the belief that engineering and computer science are stronger together, and that technical sales is where the combination delivers its greatest value, he says.

    The value of IEEE

    Prasad first encountered IEEE at a student branch meeting he attended at Bangalore University in 2000, shortly before graduation. The meeting featured engineers from industry discussing the work they did—which sparked his interest in joining, he says. But with his first job waiting for him, the timing wasn’t right to become active with the organization.

    It took nearly 25 years, he says, before he felt he had enough spare time and professional experience to contribute actively and meaningfully to IEEE. He joined the Southeastern Michigan Section in 2024, was quickly elevated to senior member, and then took on a leadership role.

    He was nominated to be conference chair for this year’s Innovative Applications of AI in Industry event. Together with a team of eight, he led the planning and execution of the in-person conference, the first time it was held since the COVID-19 pandemic shelved it.

    The event explored how AI is permeating practically every aspect of our lives. Speakers came from Amazon, Torc Robotics, academia, and health care.

    The event was a success, he says, and he hopes to parlay its momentum into a multiday conference in the coming years.

    As a representative from the section, he served as a technical judge at this year’s Robofest, a competition held in May for students in Grades 4 through 12. Since the annual event’s inception, more than 40,000 students from 35 countries have participated. He says his involvement helps him understand how students use robotics to solve problems.

    “I think that being part of the IEEE community is a huge value for folks in the engineering space,” he says. “It’s a great way to collaborate and to understand what’s happening, especially in your local ecosystem. There’s always something going on in terms of a conference or a talk where you can listen, gain knowledge, and network. It’s also an invaluable opportunity to discover where you can add value at IEEE.”

26. Why Does a Bank Need a Chief Scientist?

    

    This article is brought to you by Capital One.

    After five years leading natural language understanding and eventually the entire Alexa AI organization at Amazon, Prem Natarajan made a nontraditional move: He became Chief Scientist at a bank. Not just any bank: Capital One, a financial institution serving over 100 million customers, helping everyday Americans manage their financial lives.

    For Natarajan, a veteran of DARPA-funded research and academia who had watched machine learning evolve from task-specific applications to foundation models, the logic was clear. Some of the most interesting advances in AI research and deployment were shifting from big tech’s horizontal platforms to industry verticals like finance, where the most complex problems aren’t just building models but making AI work under the constraints of real-world customer problems, contextual business knowledge, continuous learning, with an incredibly high bar for accuracy and privacy.

    That’s also what made Capital One the right place to do it. For decades, the company has been recognized as one of the most data- and analytics-driven financial institutions in the industry. Its business model from the very beginning was built around using data and technology to personalize financial products for customers. A decade ago, Capital One went all in on the cloud and rebuilt its data ecosystem, creating a unified environment for data, compute, and AI and machine learning experimentation. Today, its modern infrastructure, disciplined approach to governance, and deep bench of talent form the foundation that allows it to lead in enterprise AI.

    Advances in AI research and deployment are shifting from big tech’s horizontal platforms to industry verticals like finance.

    So, why does a bank need a Chief Scientist? The answer lies in a fundamental misconception about AI in financial services. Most financial institutions still view AI as a technology to deploy – leveraging the latest large language model, deploying it through APIs, and integrating it into existing workflows – rather than a scientific discipline. Capital One is doing something different: building a scientific community and research organization to solve real-world customer problems and invent impactful AI solutions that don’t yet exist.

    While widely available foundation models can handle general tasks, they can’t yet solve many domain-specific challenges, such as detecting fraud in real-time across billions of transactions, or providing state-of-the-art conversational tools so customers can engage when, how, and where they want to.

    These challenges of making AI reliable, scalable, and well governed require original research and scientific innovation that is funneled back into the business to create real-world applications to address customer needs.

    The Constraints That Demand Innovation

    Prem Natarajan, an IEEE Fellow, is Chief Scientist at Capital One. “If you want to solve really important problems in AI and see your work come to life, this is one of the few places you can do that,” he says.Capital One

    Because banks are dealing with people’s finances, there is an incredibly high bar for getting it right when it comes to AI. Take fraud, for example. Even a minor fraud event can have a devastating impact on certain customers. The best fraud models and platforms can detect and help mitigate fraud in the time it takes someone to tap their card, which is table stakes for protecting customers and their financial information with accuracy and speed. Looking at these types of challenges, Capital One and Natarajan saw that serving millions of customers meant solving AI problems at a scale and complexity that many enterprises don’t encounter. These same constraints create a unique research environment.

    At Capital One, the approach to building AI is to provide value to customers in ways never possible before, improving their financial lives and meeting them where they are with services they actually need. That focus, combined with massive scale and world-class risk management requirements, makes the scientific problems both harder and just as consequential as those found in most big tech labs.

    Advancing AI Through “Destination-Back Thinking”

    Capital One’s approach to AI research and innovation starts with what Natarajan calls “destination-back thinking.” Rather than asking what’s possible with current technology, the team envisions the customer experience they want to deliver – perhaps a car buyer who works long days and can only research the options at 10 p.m., or a customer facing an unexpected expense who needs immediate, personalized guidance – and then works backward to identify the scientific breakthroughs required to get there.

    “You’re thinking back from where you’re providing incredibly valuable services,” Natarajan explains. “Once you have that vision clearly, you work back and say, what are the gaps? What are the things we need to invent?” This ensures that when problems are solved, the impact is essentially guaranteed, because the team has already identified what will make a tangible difference in customers’ lives.

    But methodology alone isn’t enough. Capital One’s nearly 15-year bet on cloud-first architecture created something rare in financial services: a unified data and compute ecosystem that can support the kind of scientific experimentation typically seen in big tech research labs. As the only major U.S. bank to go all-in on public cloud infrastructure, Capital One eliminated the legacy systems that can constrain AI research at most financial institutions. This modern tech stack enables rapid iteration, large-scale model training, and what Natarajan calls “continuous learning,” systems that improve after deployment rather than degrading over time. This unique approach to infrastructure is a critical component in making new categories of research possible.

    Agentic AI: From Research to Production

    The research agenda manifests in systems already serving customers. Early last year, Capital One launched what may be the first fully agentic AI customer service experience built entirely in-house by a bank: a car buying tool that takes actions on behalf of customers based on their requests, not just answers questions. Behind it lies extensive research into multi-agentic AI reasoning systems that can navigate real-time data, business knowledge, constraints, and guardrails, with various agents that can work together to accomplish complex tasks.

    Capital One has launched a fully agentic AI customer service experience powered by extensive research into multi-agentic reasoning systems that can navigate real-time data.

    The team is also working on solving things like tokenization challenges, protecting sensitive data while enabling model training. To accelerate this cutting-edge work, Capital One has established partnerships with Columbia University, the University of Southern California, and the University of Illinois, and became the only bank funding NSF’s national AI research centers in 2025, investing millions in initiatives that span mental health, materials discovery, science, technology, engineering, and mathematics education, human-AI collaboration, and drug development.

    In the spring of 2026, the company hosted its inaugural AI Symposium to deepen connections and foster insight-sharing between the scientific AI community, leading AI labs, startups, and its own technology, science, and AI leaders and partners.

    Building a World-Class AI Organization

    

    Capital One is building the next generation of AI talent. Join the team inventing impactful AI solutions to shape the future of finance. Learn more at https://capitalone.science/

    External validation suggests the strategy is working. Evident AI ranked Capital One as the leading bank in AI talent and a global leader in AI innovation for three consecutive years, noting the bank accounted for 38 percent of all AI patents filed by the top 50 financial institutions. Capital One was also recognized by IFI Insights as the only financial institution among the top U.S. patent leaders in agentic and generative AI in 2025, alongside the likes of Google, NVIDIA, DeepMind, IBM, Microsoft, Intel, Adobe and Samsung. Capital One’s AI team – which has experience from leading AI labs and top universities – represents expertise rarely found outside Silicon Valley.

    But recruitment requires a mission. “If you want to solve really important problems in AI and see your work come to life, this is one of the few places you can do that,” Natarajan says. The pitch is consistent: Capital One isn’t just optimizing algorithms for niche financial applications like high frequency trading, it’s using science to enhance financial experiences for over 100 million everyday Americans, expanding engagement and real-time insights, personalization, and access to their personal finances and products like never before.

    Capital One was recognized as the only financial institution among the top U.S. patent leaders in agentic and generative AI in 2025, alongside the likes of Google, NVIDIA, DeepMind, and Microsoft.

    The frontiers Natarajan is most excited about – agentic AI systems that can dramatically improve performance by reframing how problems are solved, and domain-specific reasoning that understands contextual and financial nuance – represent the next phase of innovation. “By just casting the problem in an agentic framework, you can actually get way more performance” from the same underlying models, he explains.

    It’s this kind of applied research, like translating general capabilities into production systems for millions of customers, that defines the Chief Scientist’s mandate. When recruiting talent to his AI team, a group comparable only to the most sophisticated tech companies in caliber, Natarajan frames the opportunity around a mission. He invokes Steve Jobs’ famous challenge to John Sculley: “Do you want to spend the rest of your life selling sugared water, or do you want to change the world?” For Natarajan, the parallel is clear. Building AI systems that transform financial services for millions of everyday Americans – that’s changing the world. And it requires the kind of scientific rigor that only a Chief Scientist can lead.

27. How IEEE Awardee Karen Panetta Became Bewitched by Engineering

    

    When considering the 1960s sitcoms Bewitched and I Dream of Jeannie, both of which featured women with supernatural powers navigating life with mortals, most people wouldn’t connect them with pursuing an engineering career. But Karen Panetta did. The sitcoms’ main characters—Samantha Stevens, a witch; and Jeannie, a genie—were “strong, empowered female leads using magic,” Panetta says, and they inspired her to become an engineer, as it was like sorcery to her.

    Panetta, an IEEE Fellow, is dean of graduate education at the Tufts University engineering school, in Medford, Mass., outside of Boston.

    Karen Panetta

    Employer

    Tufts University, in Medford, Mass.

    Title

    Dean of the engineering school’s graduate education

    Member grade

    IEEE Fellow

    Alma maters

    Boston University and Northeastern University in Boston

    Like Samantha and Jeannie, Panetta has made magic happen, such as when she helped to invent the first CPU digital-twin simulator. Digital twins are computer simulation programs that track and adjust the operations of a physical device in detail. Her simulator has been adapted for several industrial uses, including by NASA to help design spacecraft.

    Panetta also mentors young women to encourage them to pursue a STEM career through the Nerd Girls program she launched at Tufts in 2000. Engineering undergraduate students work on technology for socially conscious projects such as environmental cleanup, renewable energy, and the development of assistive devices to improve mobility for people with disabilities.

    Panetta received this year’s IEEE Mildred Dresselhaus Medal for “contributions to computer vision and simulation algorithms, and for leadership in developing programs to promote STEM careers.” The award, sponsored by Google, was presented at the IEEE Honors Ceremony on 24 April in New York City.

    Receiving the medal is particularly special to Panetta, she says, because she knew its namesake: Mildred Dresselhaus, an IEEE Life Fellow who pioneered the study of carbon nanostructures at a time when researching physical and material properties of commonplace atoms was unpopular. She was a MIT professor of physics and electrical engineering, and died in 2017.

    Panetta nominated Dresselhaus for the IEEE Medal of Honor, which she received in 2015.

    “Millie was a rock star,” Panetta says. “I can’t think of another medal that really encapsulates her spirit and what I’ve dedicated my life to.”

    Finding a creative outlet in engineering

    As a child growing up in Boston, Panetta built trapdoors and other features in her treehouse, she says.

    “I also explored fashion and sewed my own clothes,” she adds. “I wasn’t very successful, but I was very creative.”

    She was a top performer in math and science classes in high school, so her father encouraged her to pursue civil engineering.

    “I didn’t know what an engineer was, and my father, who was a mechanic working on heavy construction equipment, only knew about civil engineers,” Panetta says. “I started taking computer programming classes at school, but knowing how to type on a keyboard and make a software program wasn’t good enough for me. I wanted to know what was inside the box.”

    Her thirst for knowledge inspired her to pursue a bachelor’s degree in computer engineering at Boston University.

    “My father was very disappointed that I didn’t pick civil engineering,” she says, laughing.

    She commuted to school, and she struggled to find study groups for her classes, so she joined IEEE to connect with peers.

    She became active in the university’s student branch, organizing events including the IEEE Student Professional Awareness Conference, which helps students learn practical career skills including résumé building, interviewing, and networking. She organized a SPAC for her branch, and IEEE Life Senior Member Jim Watson volunteered to speak at the event. It changed her life, she says.

    Watson was the director of commercial and industrial marketing at Ohio Edison in Akron, where he worked for 36 years.

    “He flew to Boston to speak at our event, but fewer than 20 students attended. I was embarrassed,” Panetta says. But Watson told her the important lesson was that she showed up and organized the event.

    “He said I would be successful because of that,” she says. “He didn’t care about the attendees’ grade point averages, only that we were professional enough to organize the talk.

    “That encouragement was the first time anyone outside of my family ever told me that I would succeed, so it was reaffirming. To this day, I still use some of the techniques that I learned in his presentation in my own classroom to teach students.”

    Panetta graduated in 1986. Her IEEE membership helped her get hired for her first dream job: a diagnostic engineer at Digital Equipment Corp.

    While attending the IEEE Computer Society’s annual symposium on very large-scale integration in Boston, she handed her résumé to a DEC representative, who hired her to work in Hudson, Mass.

    While working full time, Panetta attended Northeastern University, in Boston, as a part-time graduate student. She earned a master’s degree in electrical engineering in 1988.

    Developing the first CPU digital twin

    In the early 1990s, Panetta was assigned to work with Ernst Ulrich, one of DEC’s most respected consulting engineers, she says. He was developing a new CPU using millions of CMOS transistors.

    “I thought, ‘Wow, what a great opportunity,’” she says, “not realizing they assigned it to me because no one else wanted to work with him, as he set rigorous standards, expecting those who worked with him to think outside of the box and hold their own to bullet-proof new concepts.”

    Panetta and Ulrich wanted the ability to test the CPU while still designing the hardware and software. That way, both would be ready to use at the same time. Typically, the hardware was developed before the software was written.

    “We decided that we were going to simulate the machine to see how it was going to run—which was unheard of,” she says.

    During a meeting with the company’s top engineers, Panetta shared her idea for an algorithm that could accomplish the team’s goal. She was met with silence.

    “It’s going to be the engineers who better society because we know how to work together. We’ve proven that IEEE members know how to work across geographic boundaries, ethnic boundaries, and gender boundaries. And that’s a good model for the world.”

    “I thought to myself, ‘Did I just say something stupid?’” she says. “But then, the top engineer looked at me and said, ‘I have been doing this for 50 years, and you, a kid just out of school, comes up with this [solution] like it’s obvious.’”

    Her idea became the basis for the digital twin simulator. It used behavioral models to run software on a CPU simulation. The software passes information through the system, she says, just like it would pass information through wires or interconnects.

    “We did successfully have a complete model of millions of transistors,” Panetta says. “I efficiently simulated hundreds of thousands of experiments and ran the software on this simulated model so that we knew exactly how it was going to perform on the real machine. That had never been done before.”

    Her groundbreaking work led to a promotion: from computer analyst to principal software engineer.

    When she began managing a team and hiring staff members, Panetta noticed the younger employees knew the theory but didn’t have the technical skills to hit the ground running, she says.

    “It took the company two years to train somebody before they could really contribute technically to a team,” she says. She decided she wanted to help prepare students for jobs in industry.

    In 1995 she was accepted into DEC’s Engineers and Education program, in which full-time employees who wanted to teach could take a leave of absence to complete a degree while still being paid. Participants were then placed in academic institutions for two-year stints to help students bridge the gap between classroom theory and real-world problem-solving.

    After earning a Ph.D. in electrical engineering from Northeastern in 1994, Panetta began her teaching assignment at Tufts. After one year, she left her job at DEC to join the university as its first female electrical engineering professor. At the time, the department had only one female undergraduate EE student.

    “I showed up to work dressed in an all-pink suit,” she says, laughing. “Other professors looked at me like I didn’t belong there because I looked different.”

    She didn’t let that stand in the way of reaching her goals: preparing the next generation of students for jobs and mentoring young women who were interested in becoming engineers but who felt they wouldn’t be accepted and therefore couldn’t pursue a career in the field.

    Launching the Nerd Girls program

    When Panetta began teaching, she noticed that students weren’t getting any hands-on engineering experience, so in 1996 she created an internship program. It was the precursor to Nerd Girls.

    At the time, she was consulting for NASA’s data visualization and animation lab in Langley, Va., translating complex information into a user-friendly animated form. The programs visualized Earth’s atmosphere and identified pollutants, their origins, and their effects on people and the environment.

    Panetta needed a larger team to help conduct the research, so she asked her undergraduate students if they wanted to participate.

    “Female students flocked to me because they could relate to the work I was doing, loved how their skills could benefit humanity, and didn’t see me as the classic nerd professor with no life,” Panetta said in a 2008 interview with The Institute about the program. “Eventually, the girls outnumbered the boys.”

    “The research project ended up winning awards,” she added. “Tufts couldn’t believe that undergrads had a hand in it. That’s when things really turned around.”

    Nerd Girls officially launched at Tufts in 2000 as a class where students work closely with industry on engineering projects. Examples have included building a solar-powered car, developing a battery for the last functioning twin lighthouse in the United States, and creating devices to help people train service animals.

    “Everyone who has participated in the program graduated with a bachelor’s degree,” Panetta says. “I’m also very proud that 98 percent of participants pursue a graduate degree within three years of earning their bachelor’s.”

    The program is open to all students, regardless of gender.

    Creating a community at IEEE

    Panetta became an active IEEE volunteer in 2004 after meeting Arthur Winston, the IEEE president at the time. Winston, an IEEE Life Fellow, was an electrical engineering professor at Tufts. He helped found the Gordon Institute, a leadership-focused engineering school at the university.

    “I sat next to him on a bus, and he invited me to attend the IEEE Boston Section meetings,” she says.

    Panetta eventually was elected by the section as a member-at-large—which allowed her to attend conferences and other events.

    To help spread the word about the Nerd Girls program throughout IEEE, Winston connected Panetta to Mary Ellen Randall, who was chair of IEEE Women in Engineering at the time. Randall is the current IEEE president and CEO. Panetta joined IEEE WIE and was elected as its 2007–2009 chair.

    In that position, she worked with Randall and Leah Jamieson, the 2007 IEEE president, to hire more staff to support the program and launch its magazine.

    “At that time, we didn’t have any way to connect to members or tell the stories of women in technology,” Panetta says. “I wanted people to read the stories of women from around the globe and how they overcame adversity. So I launched the IEEE Women in Engineering Magazine in 2007.”

    Panetta serves as the award-winning publication’s editor in chief, and she is a member of several other IEEE societies and committees.

    IEEE is helping to change the world for the better, she says.

    “It’s going to be the engineers who better society,” she says, “because we know how to work together.

    “We’ve proven that IEEE members know how to work across geographic boundaries, ethnic boundaries, and gender boundaries. And that’s a good model for the world.”

28. War Taught this Ukrainian Entrepreneur the Value of Resilience

    

    Salome Mikadze-Struk is no stranger to adversity. The daughter of refugees, she built a software-development business as an undergraduate at the height of the COVID-19 pandemic and kept it running despite the outbreak of war in her native Ukraine. Now, she’s drawing on her experiences to mentor tech-startup founders and speak publicly about the importance of resilience in entrepreneurship.

    Mikadze-Struk was studying at Georgetown University, in Washington, D.C., when COVID-19 struck. Classes went online, and she moved back to Ukraine. In the midst of that disruption she saw an opportunity to develop her business idea, called Movadex, by tapping Ukraine’s pool of talented young engineers. Then Russia invaded in early 2022, during her final semester. Taking online classes from bomb shelters and helping employees evacuate to safer parts of the country was surreal, she says, but the team kept the company afloat and she graduated later that year.

    In 2023, Mikadze-Struk took a hiatus from her business to pursue an MBA at Stanford University, which she completed this year. In her precious spare time she’s been advising startups and giving talks, using her unique perspective to promote the need for resilience in entrepreneurship—something she thinks is increasingly important in the software industry as AI coding tools upend old business models.

    “You need to be okay with risk, you need to be resilient. You need to be okay with disruption and okay with uncertainty,” she says, “because this is inevitably going to be part of this industry for the foreseeable future.”

    An Early Focus on Education

    Mikadze-Struk’s parents had settled in Ukraine after fleeing conflict in the Abkhazia region of Georgia in the early 1990s. “They left everything behind,” she says. “You can look on Google Maps and zoom in on where their houses were and it’s all rubble.”

    Despite this backstory, Mikadze-Struk says she and her sister had a conventional middle-class upbringing in Kyiv. Her father ran a small shop and her mother was a stay-at-home mom. Her parents placed an emphasis on education and encouraged her to study hard and take part in extracurricular programs such as Ukraine’s Junior Academy of Sciences, which introduces students to research.

    “They weren’t rich, so they knew that our way to make it in life was not through investments, but through merit-based accomplishments,” she says.

    When Mikadze-Struk was 14, her family discovered the newly launched Ukraine Global Scholars program, a nonprofit that helps talented students secure scholarships abroad. The program helped her win a full scholarship to the Emma Willard School, a private girl’s school in Troy, N.Y.

    Discovering Tech

    After graduating high school in 2018, Mikadze-Struk was accepted to Georgetown to study business administration. But it was outside the classroom that her career direction began to take shape. She won a startup competition with a medical device she had developed for a school project and, while the business idea didn’t go anywhere, it sparked an interest in entrepreneurship.

    Ukraine’s software industry was booming, and she began attending startup events and competitions in her home country the summer before starting college. There she met her eventual cofounder Nor Newman.

    Despite both being just 18, they saw a gap in the market. The pair noticed many founders had strong ideas but lacked the technical expertise to realize them, while talented engineering students often struggled to gain real-world experience. Newman had begun informally connecting startups with his college friends, but the pair soon saw commercial potential. “We realized we could actually create our own startup studio and help startups as a team, versus just connecting people,” says Mikadze-Struk.

    Then, when the COVID-19 pandemic struck in early 2020, halfway through her sophomore year, it brought both disruption and opportunity for Newman and Mikadze-Struk. While travel restrictions and lockdowns made life complicated, there was also a surge of companies looking to move their business online. “COVID really skyrocketed everything we were doing,” she says.

    Sensing an opportunity, Mikadze-Struk and Newman incorporated Movadex in Ukraine in early 2020. From the start, they decided to focus on not only providing engineering talent, but also helping startups with product development. Many times, says Mikadze-Struk, a founder’s vision for the software doesn’t line up with what users actually want. “What really helped us grow is not just the engineering or quality of code, but rather a holistic approach to creating a product and actually getting into the brain of the user,” she says.

    Navigating Adversity

    Back in Ukraine, Mikadze-Struk had to juggle this booming business with studying remotely—taking classes at night and working during the day. It was exhausting, she says, but it also allowed her to immediately apply what she learned in business classes to building her startup.

    Having successfully navigated the pandemic, Mikadze-Struk was dealt another wild card. In early 2022, Russia invaded Ukraine and her life was again turned upside down. It was particularly traumatic for her family, having already been forced from their home in Georgia once by war.

    In 2023, Mikadze-Struk took an extended leave from her company to pursue an MBA at Stanford.Christie Hemm Klok

    “For my parents to experience their daughters going through all the same things they had gone through was really heartbreaking,” she says. “But at the same time, because I’d heard so much about their story of resilience I had power in me to not fully break down.”

    On the day of the invasion the founders told employees to take the day off and emailed clients to warn of potential disruptions. The next couple of days were spent checking on staff and evacuating as many as possible to their headquarters in Lviv, in Western Ukraine.

    By the following Monday the business was back up and running. Soon afterward, they partnered with the Lviv IT Cluster business association’s nonprofit arm to help resettle refugees from the eastern part of Ukraine, where strikes were focused, and offer job placements. Throughout this period, Mikadze-Struk was also completing her final year at Georgetown remotely. “Half of my senior year was actually spent in bomb shelters,” she says.

    Promoting Resilience in Entrepreneurship

    That summer, Mikadze-Struk graduated with a bachelor’s degree in business administration and learned she had been accepted onto Stanford University’s MBA program. In 2023, she took an extended leave from Movadex and moved to California. She also gave birth to her daughter in 2024.

    Balancing studies and parenthood was already a full-time job, but she continued to engage with the startup ecosystem by volunteering as a startup mentor and public speaker. Now, after graduating from Stanford, she is stepping back into a more active leadership role at Movadex, where she hopes to drive the company’s expansion into the United States. She also wants to develop a stronger focus on helping customers understand and implement AI in their businesses.

    While AI is undeniably disrupting the tech industry, Mikadze-Struk, now an IEEE Senior Member, is fundamentally optimistic about its impact. “The way AI democratized access to building software and to prototyping…is just mind blowing,” she says.

    But it will require a significant shift in mind-set for engineers, especially junior developers hunting for jobs. They need to “fall in love with AI” and embrace it as a powerful copilot, she says. As these tools increasingly take over the nuts-and-bolts work of coding, engineers also need to nurture higher-level skills like systems thinking and architectural design.

    Perhaps most importantly, given the rapid pace at which the technology is evolving, engineers need to nurture their adaptability and resilience. “It’s both exciting and scary, because you don’t know what tomorrow will bring.”

29. IEEE Rolls Out Large Language Models Virtual Training Course

    

    Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.

    While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. As the AI models move into mainstream engineering practice, the demand for technical expertise is rising.

    The LLM technology market is expected to grow by about 33 percent every year through 2030, according to MarketsandMarkets. The rapid expansion suggests that proficiency in implementing and securing the models is transitioning from a niche into a core requirement for technologists.

    More than just a better search engine

    To use LLMs effectively, technical professionals must move beyond treating them as conversational robots. At a fundamental level, the AI systems are built on the transformer architecture, a framework that replaced the older method of processing data in a fixed, sequential order. Unlike earlier models that analyzed information one step at a time, transformers use self-attention mechanisms to ingest vast datasets simultaneously.

    For technical professionals, LLMs are core architectural elements that are fundamentally changing how digital infrastructures are built and maintained.

    Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.

    Four ways LLMs are changing jobs

    Here are areas that integrate large language models.

    Moving past basic prompts.Developers are using application program interfaces (APIs) to connect LLMs directly to their databases and software tools. Employing the APIs allows AI to perform work such as executing code or searching through internal repositories.

    Fixing the “hallucination” problem.LLMs are at risk of hallucinations, which are generated facts or code that looks correct but actually is wrong or broken. To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a company’s database.

    Prioritizing data security.When using AI with proprietary code, security is a major concern. Engineers must learn how to set up “private” instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.

    The future of collaboration.By automating repetitive coding tasks and summarizing thousands of pages of documentation, LLMs let engineers spend more time on high-level designs and solving important issues.

    Online course program helps with mastering the tech

    The gap between people who use AI and those who understand how to build with it is growing wider. To help technical professionals stay ahead, IEEE offers a five-course online program, Large Language Models Demystified, available through the IEEE Learning Network.

    The program, developed by IEEE Educational Activities in partnership with the IEEE Computer Society, is built for people who want to understand the “how” and the “why” behind the technology. Rather than just teaching basic prompting, the curriculum dives into the engineering behind generative AI, including:

    • Evolution, impact, and hands-on exercises:the shift from statistical methods to modern transformers, including hands-on model optimization.
    • Understanding transformer architectures: the mathematical core of self-attention and positional encoding, implemented in NumPy and Python.
    • Architectural analysis and implementation: advanced LLM design with practical model-building exercises.
    • Training and modeling with PyTorch: end-to-end pipelines in PyTorch, leveraging parameter-efficient techniques such as low-rank adaptation and quantization.
    • Optimization, alignment, and deployment: performance scaling, reinforcement learning from human feedback (RLHF), group-relative policy optimization, RAG, and agentic AI.

    Upon completion of the program, participants earn professional development credits and a digital badge from IEEE to verify their expertise.

    Enroll in the course program on the IEEE Learning Network.

    Organizations looking to prepare their teams to work on LLMs can connect with an IEEE content specialist to discuss group enrollment and tailored training paths.

30. Students Tackle Environmental Issues in Colombia and Türkiye

    

    EPICS in IEEE, a service learning program for university students supported by IEEE Educational Activities, offers students opportunities to engage with engineering professionals and mentors, local organizations, and technological innovation to address community-based issues.

    The following two environmentally focused projects demonstrate the value of teamwork and direct involvement with project stakeholders. One uses smart biodigesters to better manage waste in Colombia’s rural areas. The other is focused on helping Turkish olive farmers protect their trees from climate change effects by providing them with a warning system that can identify growing problems.

    No time to waste in rural Colombia

    Proper waste management is critical to a community’s living conditions. In rural La Vega, Colombia, the lack of an effective system has led to contaminated soil and water, an especially concerning issue because the town’s economy relies heavily on agriculture.

    The Smart Biodigesters for a Better Environment in Rural Areas project brought students together to devise a solution.

    Vivian Estefanía Beltrán, a Ph.D. student at the Universidad del Rosario in Bogotá, addressed the problem by building a low-cost anaerobic digester that uses an instrumentation system to break down microorganisms into biodegradable material. It reduces the amount of solid waste, and the digesters can produce biogas, which can be used to generate electricity.

    “Anaerobic digestion is a natural biological process that converts organic matter into two valuable products: biogas and nutrient-rich soil amendments in the form of digestate,” Beltrán says. “As a by-product of our digester’s operation, digestate is organic matter that can’t be transferred into biogas but can be used as a soil amendment for our farmers’ crops, such as coffee.

    “While it may sound easy, the process is influenced by a lot of variables. The support we’ve received from EPICS in IEEE is important because it enables us to measure these variables, such as pH levels, temperature of the reactor, and biogas composition [methane and hydrogen sulfide]. The system allows us to make informed decisions that enhance the safety, quality, and efficiency of the process for the benefit of the community.”

    The project was a collaborative effort among Universidad del Rosario students, a team of engineering students from Escuela Tecnológica Instituto Técnico Central, Professor Carlos Felipe Vergara, and members of Junta de Acción Comunal (Vereda La Granja), which aims to help residents improve their community.

    “It’s been a great experience to see how individuals pursuing different fields of study—from engineering to electronics and computer science—can all work and learn together on a project that will have a direct positive impact on a community.” —Vivian Estefanía Beltrán

    Beltrán worked closely with eight undergraduate students and three instructors—Maria Fernanda Gómez, Andrés Pérez Gordillo (the instrumentation group leader), and Carlos Felipe Vergara-Ramirez—as well as IEEE Graduate Student Member Nicolás Castiblanco (the instrumentation group coordinator).

    The team constructed and installed their anaerobic digester system in an experimental station in La Vega, a town located roughly 53 kilometers northwest of Bogotá.

    “This digester is an important innovation for the residents of La Vega, as it will hopefully offer a productive way to utilize the residual biomass they produce to improve quality of life and boost the economy,” Beltrán says. Soon, she adds, the system will be expanded to incorporate high-tech sensors that automatically monitor biogas production and the digestion process.

    “For our students and team members, it’s been a great experience to see how individuals pursuing different fields of study—from engineering to electronics and computer science—can all work and learn together on a project that will have a direct positive impact on a community. It enables all of us to apply our classroom skills to reality,” she says. “The funding we’ve received from EPICS in IEEE has been crucial to designing, proving, and installing the system.”

    The project also aims to support the development of a circular economy, which reuses materials to enhance the community’s sustainability and self-sufficiency.

    Protecting olive groves in Türkiye

    Türkiye is one of the world’s leading producers of olives, but the industry has been challenged in recent years by unprecedented floods, droughts, and other destructive forces of nature resulting from climate change. To help farmers in the western part of the country monitor the health of their olive trees, a team of students from Istanbul Technical University developed an early-warning system to identify irregularities including abnormal growth.

    “Almost no olives were produced last year using traditional methods, due to climate conditions and unusual weather patterns,” says Tayfun Akgül, project leader of the Smart Monitoring of Fruit Trees in Western Türkiye initiative.

    “Our system will give farmers feedback from each tree so that actions can be taken in advance to improve the yield,” says Akgül, an IEEE senior member and a professor in the university’s electronics and communication engineering department.

    “We’re developing deep-learning techniques to detect changes in olive trees and their fruit so that farmers and landowners can take all necessary measures to avoid a low or damaged harvest,” says project coordinator Melike Girgin, a Ph.D. student at the university and an IEEE graduate student member.

    Using drones outfitted with 360-degree optical and thermal cameras, the team collects optical, thermal, and hyperspectral imaging data through aerial methods. The information is fed into a cloud-based, open-source database system.

    Akgül leads the project and teaches the team skills including signal and image processing and data collection. He says regular communication with community-based stakeholders has been critical to the project’s success.

    “There are several farmers in the village who have helped us direct our drone activities to the right locations,” he says. “Their involvement in the project has been instrumental in helping us refine our process for greater effectiveness.

    “For students, classroom instruction is straightforward, then they take an exam at the end. But through our EPICS project, students are continuously interacting with farmers in a hands-on, practical way and can see the results of their efforts in real time.”

    Looking ahead, the team is excited about expanding the project to encompass other fruits besides olives. The team also intends to apply for a travel grant from IEEE in hopes of presenting its work at a conference.

    “We’re so grateful to EPICS in IEEE for this opportunity,” Girgin says. “Our project and some of the technology we required wouldn’t have been possible without the funding we received.”

    A purpose-driven partnership

    The IEEE Standards Association sponsored both of the proactive environmental projects.

    “Technical projects play a crucial role in advancing innovation and ensuring interoperability across various industries,” says Munir Mohammed, IEEE SA senior manager of product development and market engagement. “These projects not only align with our technical standards but also drive technological progress, enhance global collaboration, and ultimately improve the quality of life for communities worldwide.”

    For more information on the program or to participate in service-learning projects, visit EPICS in IEEE.

    On 7 November, this article was updated from an earlier version.