2026-06-29 21:00:01
Today, you probably asked a question of a large language model, or accepted a connection suggestion on LinkedIn, or watched a recommended video on YouTube, or took a different route to work based on a traffic prediction from Google Maps. In other words, you probably used artificial intelligence. But what you might not know is how much energy that interaction consumed or why.
AI requires processing massive amounts of data, which is usually done in large data centers populated by thousands of GPUs capable of executing up to trillions of operations per second. But each of those GPUs achieves that by consuming as much as 1,000 watts apiece. For comparison, if you’ve got a newer smartphone, it probably uses less than 1 W. That kilowatt figure puts GPUs on the same level as vacuum cleaners, dishwashers, and stoves, but with the big difference that data-center processors are operating uninterrupted around the clock.
Fundamentally, a lot of this inefficiency is because GPUs are trying to simulate the workings of artificial neural networks using software and billions of transistors, which requires using energy to move massive amounts of data. What’s more, the simulated artificial neurons that make up these networks lack even a fraction of the complex computing behavior of the biological neurons that comprise the most energy-efficient computing system that we know, the human brain.
The brain is roughly one million times as energy efficient at many of the comparable tasks we set for AI. To try to approach these efficiencies, a radically different way of computing called neuromorphic engineering is seeking to build electronic components and circuits that act more like the brain’s neurons and the synapses that connect them.
Huge amounts of work have gone into making electronics operate more like biological neurons and synapses. Some research has focused on developing new, experimental devices, but they aren’t yet reliable enough to be used in large systems. Other efforts aim to implement neurons and synapses by interconnecting many complementary metal-oxide-semiconductor (CMOS) transistors—the workhorses of digital logic—to simulate a single neuron and synapse. But this approach requires so many transistors (and a few bulky capacitors) that it greatly limits the size of the system that can be constructed, making it unclear how such brain-inspired hardware could ever scale up and compete with state-of-the-art GPUs.
But all along there was an artificial neuron and a synapse—each a single device—hiding in plain sight. We found them last year. They were each made possible by an ordinary CMOS transistor—and not even a very good one at that. This is the story of their accidental discovery and their great promise for lowering the environmental footprint of AI.
Modern digital electronics is based on producing and manipulating the ones and zeros of the binary code through the operation of metal-oxide-semiconductor field-effect transistors. MOSFETs have evolved in recent years, but their classic form consists of a piece of silicon that has been doped to contain an excess of either positive (p-type) or negative (n-type) charge carriers. (CMOS logic contains transistors of both types.) The device has two terminals connected to the silicon through regions highly doped with the opposite polarity of the rest of the silicon—the source and the drain. Another terminal, the gate, sits atop the silicon that separates the source from the drain. The gate itself doesn’t connect directly to this silicon, instead resting above a thin layer of insulating dielectric.
Notably, there is a fourth terminal that attaches to the bulk of the silicon; think of this bulk terminal as connecting to the underside of the chip. It doesn’t typically get much attention, but it’s very important to our story.
When voltage is applied at the gate and the bulk terminal is grounded, charge carriers of the same polarity as the source and drain are attracted to the channel region. In the case of an n-type source and drain, that will be electrons; for p-type it will be holes. The presence of these charges forms a conductive channel that reduces the resistance between the source and the drain by several orders of magnitude, and the device switches on. As the voltage at the gate increases, this physical phenomenon produces a current signal that, when plotted against the gate voltage, rises steadily. This response is ideal for logic gates, converters, multiplexers, memories, and other digital circuits. But it is not a good fit for mimicking the behavior of a neuron.
In real neural tissue, brain cells, called neurons, consist of a cell body, a long projection called an axon, and short branching projections called dendrites. The suite of behaviors and computing this collection of components is capable of is rich and broad, but the portion that artificial neural networks hope to copy is this: When the cell body’s voltage is perturbed enough to reach a particular threshold, a self-propagating pulse of voltage, called an action potential, shoots down the axon. The axon terminates in a synapse, an electrochemical connection between the axon and another neuron’s dendrites. The action potential will then temporarily boost the voltage of this next neuron, by an amount that depends on the strength of the synaptic connection. If enough action potentials reach these dendrites in a given time—from this neuron or from others that might also form synapses there—the cell body’s voltage will surpass the threshold and trigger its own action potential.
The unusual action the authors discovered is understandable if you consider that a MOSFET contains a hidden bipolar-junction transistor.
Under normal operation, with the bulk terminal grounded, increasing voltage at the drain leads to current that increases steadily. When the voltage decreases, current follows the same sloped path. Although some pairs of electrons and holes are created by current crashing into silicon atoms, these are swept away before they can accumulate.
Adding resistance to the bulk terminal means these extra holes pile up, increasing the bulk voltage relative to the source. Once that voltage reaches a certain value, the hidden transistor activates, causing current to spike. Current remains high until the drain voltage drops past a certain point.
To get closer to the behavior of real neurons, artificial neurons should produce a current spike when a critical voltage threshold is crossed and then quickly relax back to a resting state on their own. This spike needs to be sudden—nonlinear. It should also exhibit some hysteresis; that is, the activation and relaxation voltages should be different from each other to ensure that current flows only for a certain amount of time.
What’s wanted from an artificial synapse, the thing that connects two artificial neurons, is less complicated, but equally important. The main thing is that its conductance can be electronically adjustable. The device’s conductive states should increase and decrease in a linear pattern and remain stable over time.
No single MOSFET working under the standard operation mechanism can reproduce either of these neural properties. Instead, it’s been done by combining them into complex circuits. Until now, each neuron and each synapse has been implemented by interconnecting dozens and sometimes even hundreds of MOSFETs, which is highly inefficient in terms of area, performance, and cost. To limit the amount of space needed, chips can multiplex their signals, sending them to neurons and synapses serially, but such sequential processing introduces additional delays.
Despite these area-and-time penalties on tasks such as audio processing, computer vision, or health monitoring, state-of-the-art brain-inspired microchips have achieved power reductions up to a thousandfold compared with those of GPUs or CPUs on the same task. If we could create neurons and synapses from individual devices that are readily manufacturable instead, we might target more massive implementations while maintaining energy efficiency.
Working in our laboratory in 2024, one of my students was measuring a memory circuit that consisted of one transistor and one memristor—a type of nonvolatile memory device first fabricated in 2008. The student’s memristor circuit was built from two-dimensional material atop a silicon microchip containing MOSFETs. The MOSFETs were created in a commercial foundry using fabrication technology called the 180-nanometer node, which was cutting-edge in the year 2000.
One day the student forgot to connect the bulk terminal of the transistor. What he observed was a sudden increase in current with high nonlinearity that self-relaxed when the voltage was ramped down (a phenomenon called a hysteresis loop). This was a very promising neuronlike behavior!
After a fruitless week of trying to think of an explanation for this behavior, I (Lanza) asked Pazos, then my postdoctoral fellow, to try to observe and control this phenomenon in chips without memristors. This time, we applied pulses of voltage—like the spikes a neuron would produce—instead of the ramped voltage that my student used when he first saw the peculiar behavior.
Pazos’s new data helped us understand what was going on. The key was that oft-ignored fourth, or bulk, terminal of a MOSFET. Under ordinary operation, many mobile charge carriers flitting through the channel collide with the silicon atoms, producing free pairs of electrons and holes—a process known as impact ionization. The electric field created by the potential difference between the source and the drain causes these new free electrons to drift toward the positively biased drain and the holes to move toward the bulk terminal, which is usually grounded, removing the charge without any drama.
However, when the bulk terminal of the transistor is floating—unconnected as it was in my student’s experiment—the holes produced by impact ionization cannot be driven to the ground. Instead, they accumulate in the bulk of the silicon, increasing its voltage. Then things start to get interesting.
It helps here to imagine a MOSFET as two different kinds of transistors occupying the same physical space—the intentionally constructed MOSFET and a hidden, bipolar junction transistor. A bipolar device transmits a current signal across two p-n junctions, in this case the interfaces between the source and the channel region and the channel and the drain. This signal is in proportion to a smaller current at a third terminal in between, called the base. In our experiment, that third terminal is the bulk.
To get current flowing through a bipolar transistor, you need a big enough potential difference between the base and one of the other terminals, so that current can get across the p-n junction. Let’s say this “threshold voltage” is 0.7 volts, although the real number depends on device geometry and silicon doping. In our device, that potential difference comes from those holes that were accumulating in the bulk, because it was not connected to ground. Once it reaches the threshold voltage, the device becomes sharply conductive, producing an abrupt increase of current. This sharp current increase eventually falls off once the drain voltage is lowered, because that lowering reduces the rate at which holes are generated in the bulk. The remaining excess holes recombine with stray electrons or leak away, and finally the bulk voltage falls. This cycle of hole accumulation, current spike, and hole removal gives rise to a hysteresis loop, very much like the electrical behavior of a biological neuron as it integrates ionic currents, fires a spike, and relaxes back to its resting voltage.
Initially, we observed this behavior only in a few transistors, and the relaxation time was very different for each of them. So, to try to control it better, we adjusted the resistance of the bulk terminal using a second MOSFET. Simply setting that resistance suddenly caused all the transistors to fire at the same voltage with hardly any variability. In other words, we found we could create perfect electronic neuron behavior in a single silicon transistor by controlling the bulk contact resistance. Setting the resistance can be done by doping the silicon during fabrication, but we think the two-transistor cell—where one acts as the bulk resistance—offers much greater versatility because it allows for electronic control.
We had to make sure the phenomenon would last, otherwise such a device would be useless. To our delight, every single one of the devices we tested worked over 10 million cycles. Not even one of them failed during our tests.
To be honest, we were amazed. Dozens of research groups and companies all around the world have spent many millions of U.S. dollars over the past 20 years trying to emulate these neural behaviors using experimental memristor-like devices and other things, with limited success, mainly due to reliability and cost issues. We managed it in the cheapest and most industry-standard device: the MOSFET. This result was so shocking that we decided to confirm it using microchips from a different foundry. It was successful: All the behaviors could be reproduced, and perfect yield was achieved once again.
We were happy with the results and had started the process of filing for a patent and writing up our findings for the journal Nature, when our lab made another astonishing discovery: The same kind of MOSFET could act as a synapse, too!
Recall that in ordinary operation some electrons crash into silicon atoms to create pairs of electrons and holes. We noticed that at specific values of bulk resistance a significant amount of the charge from this impact ionization would get trapped in the gate dielectric. This trapped charge interferes with the flow of current through the MOSFET, effectively changing the device’s conductance. Importantly, this new conductance is stable and adjustable at will. It was then that we realized the MOSFET could also be used as an electronic synapse.
As it was in the neuron transistor, the bulk terminal was the key. A negative bulk-source voltage drives electrons into the dielectric, decreasing conductance. A positive one pushes holes in, increasing it.
Here’s how the MOSFET synapse and the MOSFET neuron, together called a neurosynaptic random-access memory, or NSRAM, could work together to achieve a simple neural circuit: Say you had a circuit consisting of three synapse MOSFETs and a neuron MOSFET. The synapses have already been programmed as we’ve described, so that each has a different conductance. Spikes of voltage with different patterns and frequencies are applied to the gate of each of these transistors. What emerges from their drains are spikes of current with amplitudes modulated by the synapses conductance values.
The spikes converge at the drain of the neuron MOSFET. With each spike, impact ionization causes charge to build in the bulk of the silicon. Some of it will drain away, but if enough spikes arrive in a short enough period of time, the bulk voltage will reach a value at which the “hidden” transistor triggers a spike of current through the MOSFET. This current would then go on to become the input to other MOSFET synapses, and so on. The behavior is exactly the kind of integrate-and-fire action real neural circuits deliver.
The competitive advantage of our single-MOSFET electronic neurons and synapses is straightforward: We can produce with only one or two transistors the electronic signals that today require, at an industrial level, dozens and sometimes even hundreds of components. And moreover, unlike other emerging technologies, our solution is fully compatible with today’s silicon manufacturing lines and exhibits a yield of 100 percent in key figures of merit with near-zero variability.
Building functional circuits for brain-inspired computing and AI based on this technology is as exciting as it is laborious. It will require us to improve our computer models to resemble the behavior of both devices more accurately and to do so with computational efficiency. We must also perform accurate circuit- and system-level simulations to validate computing architectures, design peripheral circuitry to drive and convert signals, and undergo multiple fabrication rounds to optimize performance.
But all that will be worthwhile, because it could result in brain-inspired microchips for AI with better energy efficiencies than what we have now. These chips will first be a fit for smaller-scale, “edge-AI” tasks, such as bringing greater intelligence to battery-powered systems. But if we can scale up such chips, maybe in the long run they can compete with state-of-the-art GPUs.
2026-06-29 19:00:02
In 1839, J.M.W. Turner painted The Fighting Temeraire. The old warship, once a hero of the Battle of Trafalgar in 1805, glides like a ghost across the canvas, towed by a small steam tug belching smoke on its final voyage to the ship-breakers. The image shows a clear moment of change: sail giving way to steam, and with it, a major shift in power. The ship relied on timber, rope, canvas, and Britain’s seafaring towns. The tug depended on coal mines and iron foundries that supplied machine shops in the Midlands. Turner showed the tension of this time, when new technology changed who held power.
By Turner’s time, the United States had already defeated Britain’s navy in two wars—one for liberty on land, another for freedom of the seas. The 13 colonies used new technology in creative ways to win their freedom, and by keeping up with innovation, they managed to defend their freedom. Now, as the U.S. celebrates its 250th anniversary, we can ask: What does it really mean for a country to be independent?
We tend to focus on how nations and individuals defend freedom but rarely turn that focus to the tools and systems that sustain freedom. Declaring independence is only the beginning: independence must still be engineered.
Long before the first shots were fired at Lexington and Concord in 1775, Britain had drawn the lines of conflict through technology. The Wool Act of 1699 choked colonial textile exports. The Hat Act of 1732 crushed local hat-making. The Iron Act of 1750 forbade finished iron goods. Each statute tightened the knot: Colonial capability existed only at Britain’s discretion. The Boston Tea Party may have been a loud response, but resistance also took subtler, more empowering forms. At a 1769 Virginia ball, more than a hundred women arrived in homespun gowns. Every thread was defiance.
When war came, everyday tradespeople pivoted to the fight. Farmers turned plowshares into gun barrels, while clockmakers turned their precision skills to making firing mechanisms. By 1777, two weapons production models had emerged—centralized sites like the Springfield Armory that could produce high-quality guns in large quantities, and household workshops that were more agile and could meet local needs. In parallel, the new nation developed an equally important source of supplies and support: France sent gunpowder and loans and eventually opened a second naval front in 1781, which proved as decisive as any weapon.
After the war, the young republic pursued industrial strength with the same resolve it had shown in battle. In 1789, Samuel Slater arrived from England with textile spinning technology that he’d memorized, sowing the seeds of U.S. manufacturing, whose early growth rested on domestic cotton, slave labor, and copied techniques. By 1816, gun manufacturer Simeon North’s milling machines were producing interchangeable metal parts, allowing the armed forces to cannibalize parts. In 1822, Thomas Blanchard’s copying lathe automated the shaping of gunstocks. In the 1830s, the federal government imposed tariffs that shielded infant industries, fulfilling Alexander Hamilton’s vision for industrial policy: Build capacity first, then compete.
At the 1851 Great Exhibition in London, American revolvers and reapers with swappable parts stunned international observers. By the 1860s, land-grant colleges were spreading technical education across the nation. Engineering moved into the mainstream, from niche to national necessity, and driving broad, though uneven, prosperity. As the Industrial Revolution bloomed, the early U.S. focus on industrial capacity via farms, factories, and formidable wealth positioned the country to compete with the most advanced industrial powers in the world.
For nearly two centuries, that ethos endured, with government-guided infrastructure and markets deciding the details. But around the U.S. bicentennial in 1976, a conviction took hold across party lines. Finance began to outrank fabrication, and Wall Street prioritized futures contracts over companies owning the factories that made up their supply chains. Domestic factories closed or moved offshore, and companies turned to just-in-time manufacturing and shipping, ostensibly as a way to save on costs. Shipbuilding felt this shift as much as any industry. Shipyards closed, and suppliers of specialized castings and components disappeared along with them, as did skilled technical workers who retired without replacement. Now the U.S. Navy struggles to build submarines fast enough to replace its aging fleet.
Other changes took hold, among them the idea that the company that builds your tractor or medical equipment could prevent you from fixing it yourself. Invasive “terms of service” prevented customers from reaching for a wrench, instead allowing companies to keep reaching into customers’ pockets. These changes are symptoms of both structural and infrastructural fragility. When we lose the ability to understand and sustain the systems we rely on, we lose control—bit by bit.
No nation can build everything alone, of course. From hand-forged muskets to finely printed microchips, the sovereignty etched into our tools demands a prudent calculus: what to make at home, what and with whom to trade. Engineering is how a nation keeps its independence alive. Independence requires both the courage to innovate and the stewardship to maintain what has been built. The American Revolution was itself an act of engineering—daring in vision and deliberate in pairing anvil and alliance. Generations later, can a nation that cannot see its own dependencies, build and maintain its critical tools, or repair what breaks still call itself free?
Turner’s Snow Storm—Steam-Boat off a Harbour’s Mouth, completed three years after The Fighting Temeraire, captures this part of the story. Sea and sky dissolve into a churning vortex around the ship. Turner claimed he had himself lashed to the ship’s mast for four hours so that he could paint the sensation of standing inside a system too vast and tangled to comprehend. A nation that loses sight of what it depends on stands there too: lashed to nothing except the churn.
2026-06-27 02:00:01
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.
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.
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.
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 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.
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.”
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.
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.”
2026-06-26 01:32:32
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.
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.
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.
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.
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.
2026-06-25 21:00:01
In the mid-noughties, when music by the Killers and Franz Ferdinand blared out of every pub and nightclub I passed, I spent my days and nights struggling through a Ph.D. in applied mathematics. My research focused on simulating how special light waves interact in liquid crystals and using simple equations to approximate and understand those interactions. When I look back at my thesis now, liquid crystal technology is old hat, and I imagine my work could be completed with AI assistance in a matter of days—maybe hours.
But the same cannot be said for the work of the pure mathematics Ph.D. students with whom I shared a cramped office at the University of Edinburgh. At the time, I felt sorry for these colleagues, who day after day sat at their desks, seemingly tearing their hair out and making no progress. (Though I was struggling too, I was at least always making some headway.) When we finished and went our separate ways, some hadn’t even published a paper.
Now, in hindsight, I finally understand why they toiled for years on abstract mathematical problems that only a handful of people in the world care about. It wasn’t arrogance, as I thought at the time; they weren’t trying to prove their superior intelligence by being the first to solve a seemingly intractable mathematical problem. It wasn’t even a form of masochism (which was my second guess)—penance for some imagined inadequacy. I realized they derived joy, satisfaction, and meaning from the long journey toward understanding.
“Sometimes, understanding just strikes you as being very beautiful.” —Jeremy Avigad, Carnegie Mellon University
“Sometimes, understanding just strikes you as being very beautiful. Sometimes it’s a feeling of accomplishment, like completing a marathon,” muses Carnegie Mellon University mathematician Jeremy Avigad. “But it’s not quite either of those: It’s just a wonderful feeling when you’ve been thinking long and hard about something complex, difficult, and then—all of a sudden—it just comes together.”
This feeling has driven mathematicians throughout history. Likewise, the way mathematicians pursue that feeling has changed little over the centuries. They notice or imagine links, patterns, or properties in numbers, shapes, or logical structures. From this, they write conjectures—unproven statements of their speculation. They or other mathematicians then use logical reasoning and the tools of mathematics in often creative ways to prove or disprove those conjectures. Finally, yet other mathematicians verify (or challenge) the proofs.
Invariably, this process requires a whole heap of thinking time. “I went to a pure maths camp with classes where we would sit with hard maths problems for half an hour and no one would say anything—everyone was just thinking,” says Krystal Maughan, a mathematician and computer scientist about to get her Ph.D. at the University of Vermont. “But then we would work together and kind of tease out the problem.”
This is the age-old joy of math in action. But today’s AI systems are starting to make inroads into bypassing this slow, deliberative process. Taking this trend to its logical conclusion, what happens if AI makes the mathematician’s struggle completely unnecessary? Might AI even sideline humanity completely?
For decades, computation has accelerated mathematical progress. This began 50 years ago, when mathematicians used a computer to prove the four-color theorem, which asks whether any map can be colored using no more than four colors, with no adjacent regions sharing the same color. The answer is yes, and the computer proved it, controversially, by checking 1,936 cases in a way no human could realistically verify.
Yet throughout this computational era, even in proofs relying on massive computational resources, the role of the human mathematician has remained central. Humans propose conjectures, guided by intuition. They devise strategies to prove them, guided by creativity and experience. And humans verify whether those proofs are correct.
Now AI is challenging the status quo. In just a few years, large language models (LLMs) have evolved from “stochastic parrots,” capable of little more than regurgitating basic mathematics scraped from the internet, into advanced mathematical reasoning machines.
Last summer, systems from Google DeepMind and OpenAI reached a level equivalent to the world’s most mathematically gifted high school students, achieving gold-medal status at the International Mathematical Olympiad. In this annual competition, contestants must solve six notoriously difficult problems from various areas of mathematics.
Earlier this year, Google DeepMind’s experimental AI system Aletheia achieved an even more significant milestone when it autonomously produced publishable Ph.D.-level research results. While the work itself is obscure mathematically—calculating structure constants in arithmetic geometry—the significance lies in the complex reasoning it displayed in tackling an unsolved mathematical problem. And more recently, a new general-purpose AI system from OpenAI disproved an important conjecture in combinatorial geometry. This result would have been worthy of publication in a major mathematics journal if humans had been the authors, and top mathematicians hailed the feat as a milestone for AI in mathematics, demonstrating independent, original, and sophisticated thinking.
Another shift has come from combining LLMs with mathematical tools known as proof assistants, which have been around for more than a decade. These systems—such as Isabelle, Lean, and Rocq—are specialized programming languages that check mathematical proofs step-by-step, verifying their logical correctness. Traditionally, mathematicians have had to translate their theorems and proofs into this machine-readable format by hand, a laborious process known as formalization. Now, LLMs are starting to remove this bottleneck, automating the translation of informal proofs into formal code that proof assistants can verify.
Euclid’s famous proof that there are infinitely many prime numbers appears very different when formalized in Lean, a proof assistant. Human mathematicians routinely skip steps and rely on shared understanding; formalization makes every assumption and inference explicit so a computer can verify the proof.
We want to show that for every natural number n, there’s a prime p that is at least n.
Consider the smallest prime factor of n! + 1. Call it p. It is obviously prime.
To show p is at least n, assume, for contradiction, that it is not.
p then clearly divides n!, so it also divides (n! + 1) − n! = 1.
But this is impossible: p is prime, and 1 has no prime divisors.
So p is at least n.
/- Euclid’s theorem on the **infinitude of primes**.
Here given in the form: for every `n`, there exists a prime number `p ≥ n`. -/
theorem exists_infinite_primes (n : ℕ) : ∃ p, n ≤ p ∧ Prime p :=
1let p := minFac (n ! + 1)
have f1 : n ! + 1 ≠ 1 := ne_of_gt 2have pp : Prime p := minFac_prime f1
have np : n ≤ p :=
le_of_not_ge fun h =>
have h1 : p ∣ n ! := dvd_factorial (minFac_pos _) h
3have h2 : p ∣ 1 := (Nat.dvd_add_iff_right h1).2 (minFac_dvd _)
pp.not_dvd_one h2
⟨p, np, pp⟩
❶ Definitions must be explicit. The proof formally defines p as the smallest prime factor of n! + 1 before it can use that quantity.
❷ Formal proofs build on earlier formal proofs. Here Lean invokes a previously verified theorem showing that p is prime.
❸ Hidden logical steps become explicit. A human mathematician can write that p “clearly” divides 1. Lean requires the proof to invoke a formal theorem about divisibility and show exactly why that conclusion follows.
With technical assistance from Sidharth Hariharan
Versions of such systems, sometimes called reasoning agents, are becoming highly sophisticated. In February, for example, the AI company Math, Inc. used its aspirationally named reasoning agent Gauss to formalize a proof that had earned the mathematician Maryna Viazovska, of EPFL, in Switzerland, a Fields Medal in 2022. Gauss first helped human mathematicians complete the formalization of Viazovska’s solution to the 8-dimensional sphere-packing problem in a matter of days, and then autonomously formalized the more complicated 24-dimensional case in just two weeks.
Such achievements suggest that AI is already capable of handling some mathematical tasks long considered uniquely human. As the technology advances, more of the day-to-day work of human mathematicians is likely to become fair game for AI.
Human mathematicians could become “priests to oracles.” —Yang-Hui He, London Institute for Mathematical Sciences
In September 2025, I attended the 12th Heidelberg Laureate Forum—an annual conference that brings hundreds of young mathematicians and computer scientists together with their intellectual idols. AI dominated the conversation and, from the get-go, tension was in the air.
Speakers described a future in which superhuman AI mathematicians transcend human knowledge and capabilities: forming conjectures, searching solution spaces, proving conjectures, and finally verifying the proofs and generalizing the results, all without human involvement. If this future comes to pass, Yang-Hui He of the London Institute for Mathematical Sciences memorably declared, human mathematicians could become “priests to oracles.”
While such startling predictions were being voiced on stage, my gaze was drawn to the audience. Frowning, fidgeting, and exchanging furtive glances—the crowd’s unease was palpable. Trill White, a student at Australia’s Deakin University, later recalled sitting in that hall and thinking: “ ‘That’s devastating. What will people have to contribute to mathematics? Will it become something that no one understands?’ I did get a sense that this is going to change everything.”
“We certainly started realizing AI has the potential to replace us.” —Jessica Randall, Google Developer Groups
Jessica Randall, a South African mathematician for Google Developer Groups, says she sensed a collective existential dread rising among the young mathematicians. “I could feel everyone was worried, because they hadn’t thought that far ahead,” she says. “It was like a big bombshell that hit us, and we certainly started realizing AI has the potential to replace us.”
Some established mathematicians, including He, seem comfortable with AI taking on tasks that are currently the preserve of human mathematicians. That’s because they just want to know the answers to the biggest questions in mathematics—such as the six remaining Millennium Prize Problems—even if AI does it all. “A lot of mathematicians are pragmatic and just want to understand. They would sell their soul for the solution to a problem,” jokes Avigad. “Whatever it takes, right?”
But this “just want to know” camp is by no means the only faction: Most mathematicians do not hope or expect AI to replace them entirely. Instead, two broad alternatives are emerging. The first is a human-centric aspiration that prioritizes human understanding of mathematics and treats AI as a tool, much like a calculator. The second is a collaborative “teamwork makes the dream work” vision, where humans and AI work together to tackle problems neither could solve alone.
Numbers are “a way of bringing us to agreement.” —Akshay Venkatesh, Princeton University
Fields Medalist and Princeton mathematician Akshay Venkatesh has been thinking about this topic from the human-centric viewpoint for years. In 2022, he used his Fields Medal Symposium to implore the mathematics community to deeply consider what AI might mean for the practice of mathematics. At the time, the idea that AI could replace mathematicians seemed far-fetched. Now, he says, “we’re reaching the point where, for at least some tasks with abstract mathematical reasoning, computers are becoming competitive with humans.”
For Venkatesh, the question is not just what computers can do, but what mathematics is for. “Sometimes I think when we use numbers, it’s not so much that we are describing phenomena that are intrinsically numerical, but that we can all agree exactly what the numbers mean,” he says. “It’s a way of bringing us to agreement.”
Mathematician and machine learning expert Maia Fraser, of the University of Ottawa, shares this sentiment. She says the joy she derives from mathematics is something distinctly human that integrates the subconscious and conscious mind. She describes starting with an intuitive sense that a certain thing should be true and gradually bringing out something that she can express in a rigorous proof. Communicating and sharing these deep-born thoughts is “a form of collective intelligence that is something beautiful about the human spirit,” she says.
By these arguments, an AI proof of a mathematical conjecture that has stubbornly resisted human efforts would be useful only if comprehensible to humans. “That the statement can be proved by AI is already useful information,” concedes Fraser. “But then it’s still an open problem to come up with an elegant, beautiful human proof.” Even if no such proof exists, she says, searching for it “is still a valuable endeavor.”
A more collaborative approach to AI in mathematics comes from Terence Tao, who first competed in the math Olympiad at the age of 10. In 1986, 1987, and 1988, he won bronze, silver, and gold medals, respectively, making him the youngest winner of each of the three medals in Olympiad history. Now a Fields Medalist and professor at the University of California, Los Angeles, he has earned a reputation as one of the most gifted mathematicians alive.
Unlike some of his peers, Tao is neither dismissive of AI nor fearful. Instead, he sees it as the catalyst for a fundamental shift in the discipline—a transition toward what he calls “big mathematics.” He envisions a future of large-scale, decentralized collaborations between humans and machines, where complex mathematical tasks can be diced and sliced, with humans claiming the creative parts and AI doing the lion’s share of the technical grunt work.
| AI as a tool | AI as a partner | AI as an oracle | |
|---|---|---|---|
| Role of AI | Assistant | Collaborator | Autonomous researcher |
| What matters most? | Human understanding | Shared discovery | Answers |
Already, Tao is experimenting with this concept, working on problems alongside scores of online collaborators, some using AI tools. “A hundred years ago, almost every mathematics paper was single author,” he says. “But now I collaborate with people I’ve never met—and maybe in the future, I won’t even know if they are AI or real people.”
The key to Tao’s vision is uniquely mathematical: formalization. When a proof is translated into code and checked step-by-step by proof assistants, it removes any chance of human error or dishonesty. This approach changes how collaboration works, because trust is established through verification rather than reputation or rapport. An idea from an unknown researcher or even an amateur can be taken seriously if it has a formal proof.
“If it wasn’t for this formal verification layer, opening projects up without any safeguards would just be a disaster,” adds Tao. “But in math, we can completely check and verify outputs, and this really filters out a lot of the rubbish.”
From the young researchers at the Heidelberg Laureate Forum to some of the biggest names in the field, mathematicians all seem to agree on one point: AI has the potential to transform their discipline. But there’s far less consensus on what that transformation will mean in practice.
Some worry about the accessibility of AI tools. Traditionally, mathematicians have required little more than intuition, training, and a pen and paper to advance their field. If this slow, deliberative process is no longer valued by society, and particularly by research funders, then mathematics could become an elitist activity, only practiced by select organizations that can afford to work with proprietary AI models.
Another concern is motivation. As AI systems take on more of the work, the incentive to engage deeply with difficult problems may weaken. Princeton’s Venkatesh says that the long human process of formulating and understanding a proof may be hard to justify, not just to funders, but even to mathematicians themselves. “There have been times where I’ve spent years thinking about something, and I’ve slowly struggled to understand it,” he says. “If your computer can do large chunks of that for you, will you have the motivation to spend that time?”
That concern extends to the next generation. If students can use AI to jump straight to answers, they most likely will. But every time they skip the struggle, they miss an opportunity to build the foundations of their own unique intuition. Over time, some worry, the next generation of mathematicians may suffer from a form of intellectual atrophy, unable to think outside the AI box that trained them.
In response to such fears, the mathematics community is taking action. Individuals are writing essays, organizing workshops, and debating in journals, while institutions and community groups are developing guidelines for how AI should be used in research and publication. Indeed, mathematicians are applying the same rigor and curiosity that they use every day to reckon with the challenges of AI. Taken together, these efforts reflect a broad effort to try to retain control over the direction of mathematics in the era of AI.
So, is AI sucking the soul out of math? In one way, it is doing the opposite. It is forcing mathematicians to confront deep questions about what mathematics is, why they have devoted their lives to it, and the purpose math serves in society. At the same time, though, it is reshaping the practice of mathematics in a way that may be difficult to reverse.
“Mathematics makes me a better problem solver at normal problems, because it frames my mind to think in a very logical, rational way,” says Randall, who noted the existential dread at the Heidelberg Forum. “It helps with every aspect of my life.” As AI transforms mathematics, many researchers wonder whether future mathematicians will be able to say the same.
2026-06-25 02:00:01
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.
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.”
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.
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.
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.
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.”