Q&A with Scott Acton, New Chair of UVA’s Department of Electrical and Computer Engineering
The Department of Electrical and Computer Engineering at the University of Virginia School of Engineering and Applied Science officially welcomed its newest chair, Scott Acton, on Sept. 2. Acton, a professor of electrical and computer engineering, joined UVA’s faculty in 2000. He holds a courtesy appointment in biomedical engineering and leads the Virginia Image and Video Analysis research group.
Acton has distinguished himself in research in the areas of signal, image and video processing and analysis, authoring or co-authoring more than 300 publications. The National Science Foundation, National Institutes of Health, U.S. Department of Defense and private industry have awarded grants to support his research. Signal and image processing contributes to the department’s bold ideas for biomedical systems and data science, a cross-cutting area that draws on research strengths in machine learning, devices and circuits, and cyber-physical systems.
UVA Engineering Dean Jennifer L. West announced Acton’s appointment in January 2022. “Scott saw the possibilities at the intersection of engineering and medicine and set a foundation for the School’s prominence in Engineering for Health, producing path-finding research to help medical professionals diagnose and treat disease,” West said. “Scott’s knowledge of the research enterprise and dedication to creating a meaningful student experience positions us for continued growth, not only in health but also in sustainability and the cyber future.”
For the past several years Acton has split his responsibilities between UVA and the National Science Foundation’s Directorate for Computer and Information Science and Engineering, where he served as program director. James H. Aylor, professor emeritus of electrical and computer engineering and UVA Engineering dean emeritus, served as the department’s interim chair, enabling Acton to complete his work for NSF.
We talked to Acton about his interest in electrical engineering and path to UVA, the research he has pursued at UVA and the NSF, opportunities within the field of electrical and computer engineering, and his priorities for the department in the year ahead.
Q: Tell us a little bit about your background – where you grew up and went to school, and how you became interested in electrical engineering.
A. I was born in Los Angeles. My parents moved to the East Coast when I was 3. I grew up in Vienna, Virginia, and attended Oakton High School. So I can relate to many of our students who are also from Northern Virginia.
I became interested in electrical engineering at a very young age. My dad bought a Commodore PET, one of the world’s first personal computers, in 1979, and I became fascinated with programming and computer hardware. It’s been a lifetime of computing for me. I joined the field of engineering even before I earned my college degree, working at AT&T and Mitre. For college, I went to Virginia Tech, and continue to appreciate the close collaboration of faculty and students across the Commonwealth.
What path did you take into academe?
I earned my Ph.D. in electrical engineering at UT Austin, supported by Motorola. I was able to work there during the summers and for a little bit after I graduated, so I got some semiconductor experience at a fabrication plant. Today’s market and overseas supply chains represent a huge change from the 1990s when I worked at Motorola. I am eager to see the impact that the CHIPs Act will have on domestic manufacturing of advanced chips. My first job in the academy was at Oklahoma State University. I spent half a decade there before coming back to my home state, joining UVA in 2000.
You joined UVA’s faculty just as computer vision technology was starting to gain traction. What was that like?
When I arrived at UVA, I saw untapped potential to use engineering and computer science in medicine. The computers were becoming just powerful enough to analyze images in real time. We were learning just enough about the human visual system to understand how to make algorithms to enable cameras, computers and software mimic what happens in our eyes and our brain.
A little later, we realized more attention needed to be given to computing in basic biology – looking at things at the cellular level, understanding life in the brain, how systems in biology interact. It’s an area that continues to stimulate new research directions
Images are a fascinating medium in which to work. I think it’s the mystery of the eyes and brain that fascinates me. You can meet someone once, and later see them getting a drink at a water fountain and recognize them from behind. How is that possible? There is something fascinating about the sort of inscrutable workings of the brain, and I want to unravel that mystery. We are doing that bit by bit with algorithms and signal processing as well as computer vision. During the past five years, major advances and interest in machine learning and artificial intelligence have grown a hundred-fold.
There are myriad opinions about the trajectory for artificial intelligence, from world-saving to apocalyptic. What’s your perception of AI research and where it’s heading?
Is AI going to take over the world? I don’t think so. Machine learning really isn’t in itself prescient or soulful, so I am not worried about that. I suppose I am a very cautious optimist. I am optimistic about the widespread availability of data and the growing power of computing to parse these data, to harness computing power to problem solving. But it’s important to know the limitations.
That’s one great thing about AI education. Engineering is a discipline based on design. We’re very careful about design and how we approach problem solving. It’s very traditional in that sense. Machine learning ushers in a new era, when you can download someone else’s code from the internet, run it on your data and consider it done.
We teach our students to ask questions along the way about why something works, why something doesn’t work, how it works, the most economical way of solving a problem, and what to do if something breaks.
AI is only as good as the data on which it is trained. And oftentimes that data is skimmed from the internet. The internet in some cases is a fine place, but in other places you can find a lot of junk, a lot of garbage. Training data poorly curated from the Internet could lead to an AI that reinforces its own racism. We teach our students to guard against that.
AI is just one dimension of electrical and computer engineering. What are some of the hallmarks of the ECE discipline, today and in the future?
Electrical and computer engineering, as a discipline, is perfectly aligned with two of the nation’s greatest priorities: semiconductor production and responsible development of AI. This is perhaps a coming golden era of ECE. Collaboration with computer science and data science positions electrical and computer engineers to develop the next generation of integrated circuits and bring that domain back to the United States, and to develop algorithms that lead to AI that is interpretable and responsible.
Skills and abilities unique to our discipline put electrical and computer engineers in that prime spot. It goes back to the design emphasis I just mentioned. When we look at a problem, we come up with specifications for a solution; we come up with a process; sometimes we come up with a product. This process of design, although laborious, makes us unique.
Our success, in part, comes from our heavy reliance on a mathematical foundation. ECE is well known as program that has pretty stringent mathematical requirements. Mathematicians are very successful in our field, and some of our graduates contribute to basic mathematical research as well. In addition to mathematical training, our intimate knowledge of hardware, down to the circuit and transistor level, gives us an advantage in understanding the world of computing.
How’s the view from NSF’s vantage point?
For the last three years I have worked as a program director and for a short time deputy division director within the computing wing of the NSF, formally called the Computer and Information Science and Engineering Directorate.
The opportunity to work at NSF was attractive because it was totally unlike my teaching and research. It introduced me to research administration and how to shape a vision for the future. If I had any part of that vision, it was with a program called predictive intelligence for pandemic prevention, which promotes how computing and biology engineering could help avoid or at least mitigate future pandemics.
The program was spurred by COVID, but it’s strictly and emphatically a general program for pandemic research that cannot be limited to solutions for COVID. NSF is interested in basic science. In contrast to applied research to solve a temporal crisis, basic research answers questions about how zoonotic diseases occur: Is environment a factor? Is commerce a factor? Is the response to a pandemic a question of sociology?
And I wanted to find out how the government worked, why proposals were funded and why they weren’t, and how to help faculty here at UVA Engineering. That’s a big deal for me in my role as chair. I am excited about mentoring, and can offer solid advice into funding mechanisms, helping early career faculty avoid stumbles I made when I was starting out.
I always thought program managers didn’t want to get to know me or talk to me, and they certainly didn’t want to help me, because they just wanted to reject my work. But I was wrong. I want to correct that misperception. So my first piece of advice is to get to know the people on the other side of the table. The program officers really do want to help you and want to get to know you over time. Faculty should not be shy about introducing themselves and engaging with programs at the agency where they are trying to get a foothold.
Could you say a bit about your priorities for UVA’s Department of Electrical and Computer Engineering? What are you most looking forward to, as ECE’s chair?
One of our first priorities is to revamp our undergraduate curriculum to make it more understandable, well-motivated and purposeful. For example, if I were a student interested in robotics this semester, I would have to create my own academic plan by looking through the course catalog or Lou’s List (an unofficial index of UVA courses) to pick out a couple courses of interest. We want to provide students a smooth and well-marked track they can follow based on their interests. At the end of program, that robotics student may choose to go to graduate school or join the private sector, and either way feel prepared holding a degree from UVA.
We will provide a solid educational experience and stake our claim in the AI and machine learning arena, with courses and research relevant to where the country and industry are heavily investing.
Thirty years ago, someone might go into ECE because a parent was an electrical engineer, or because they heard it was the most challenging major. The students of 2022 want to contribute to cancer research, or develop the next solar cell, or program the next Tesla. People are going into the field because they want an answer to “So what?” We’re going to try to do that with our new curriculum, to explain the “so what.” That’s priority one.
Second, we are going to focus and build on two or three stress areas for research. For the past half-century, UVA ECE has been home to pioneers in terahertz electronics, things that change within a femtosecond. We have world-class researchers in optical devices, making devices that interact with light and converting light into current, from imaging black holes to finding alternative sources of power.
UVA ECE has a strong tradition in embedded systems, what some might call cyber-physical systems, where one is embedding electronics in the form of microcontrollers or small computers into a variety of devices, like sensors for smart homes or autonomous vehicles. UVA’s Link Lab for cyber-physical systems offers a unique interdisciplinary environment. I think this program is unique in the nation, to educate and do research in the internet of things, and now the internet of a billion things.
On the medical side, we leverage UVA’s world-class School of Medicine, alongside collaborations with UVA’s College and Graduate School of Arts and Sciences in biology and chemistry. Engineering for health, which is one of UVA Engineering’s big three research areas, encompasses medical imaging, robotic surgery and devices. We’re hoping to grow this area given our proximity and close relationship with the hospital and medical school.
Diversity and inclusion round out my top three priorities. We want to form our own set of goals and priorities within the department. It’s my belief that if DEI goals are pushed from above, it’s not as useful an exercise. It’s better for faculty, staff and students to own and embrace their values and figure out techniques to get to the place they want to get to, instead of feeling that someone outside is imposing a vision upon them. This will impact hopefully the diversity of our faculty, the diversity of our students to the extent we can influence it, the diversity of our staff, and the ability to include and provide an equitable experience to everyone in our program.