Jennifer Neville is a partner research manager at Microsoft who’s built a career around understanding and advancing AI for real-world use, and much like the human-AI interactions she’s been studying, her early-career path was multiturn: math, then physics; cognitive science, then work; and finally computer science—despite her best efforts to avoid the field.
In this conversation with Principal Applied Scientist Chad Atalla, she explores the role evaluation plays in pushing the performance boundaries of today’s AI systems to meet user needs and the “surprising failures” that emerge when models are tested beyond traditional benchmarks. Neville also shares practical guidance for working with current AI systems and discusses why looking closely at data matters when results defy expectations, and what decades of AI progress have taught her about predicting what comes next.
From an unexpected career trajectory to the frontier of AI interaction and learning, this episode asks a larger question: what can we learn when the path—whether human or artificial—doesn’t unfold the way we expect?
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Transcript
[MUSIC]
JENNIFER NEVILLE: There were times that I felt like I was spinning my wheels. Things weren’t working. I would get, kind of, dejected. And then we’d get to a point where we did learn something, and it was just the, the emotional thrill of that was, that was really what hooked me. The being able to, kind of, understand something that no one else understood yet …
CHAD ATALLA: Sure …
NEVILLE: … because it’s just at the frontier of what we know about things.
STANDARD INTRODUCTION: This is the Microsoft Research Podcast, where Microsoft researchers—driving advancement through fundamental science and technology research—explore the who, how, and what’s next in computing and AI.
[MUSIC ENDS]
CHAD ATALLA: Hello, and welcome. I’m Chad Atalla, an applied scientist here at Microsoft Research.
Today, I’m joined by Jennifer Neville, a partner research manager at Microsoft Research and the Samuel D. Conte chair professor of computer science and statistics at Purdue.
Her research examines machine learning and AI for interactive domains and structured data, looking at how the data points that AI systems are trained on affect their behaviors and how that aligns with what users actually want.
Across that research, she has published more than 130 papers with over 10,000 citations and received honors such as a National Science Foundation CAREER Award, a spot on IEEE’s “10 to Watch” in AI list (opens in new tab), and best paper awards from the International Conference on Data Mining (opens in new tab) and the International Conference on Learning Representations.
What amazes me most about Jen’s work is her foresight and ability to have a through line that stays consistent as the field of AI develops, and I’m excited to hear more about how she got to where she is today.
Jen, thank you for joining me.
JENNIFER NEVILLE: Thanks for having me.
ATALLA: Awesome. Well, I would love to learn about how you got here today. And let’s rewind way back. When I was a kid, I wanted to be an astrophysicist, but of course, here I am with a computer science background. When did you know that you wanted to go into computer science?
NEVILLE: That’s a good question. I … when I was growing up, I wanted to do anything but computer science because that’s what my dad was in, and I wanted to do anything but what my dad did. So when I went to college, first I majored in math and then in physics, but I really just couldn’t vibe with those majors.
So I dropped out of college for a while, and when I went back to college again, I majored in cognitive science instead, which also was too squishy for me. Didn’t have enough math in it. At that point, if somebody had told me, “AI is the thing you should be doing because it combines the cognitive science with the math and computational thinking,” I think I would have saved myself a lot of time, [LAUGHTER] but that didn’t happen.
And so I worked for a while. And then when I went back to school, I decided to major in computer science because what I wanted to do was think about working with data and dealing with data. And that’s when I found AI. Just by happenstance. I didn’t even really realize that it was part of computer science.
ATALLA: Awesome. Well, it’s one thing to go into computer science and want to work on data or AI and another thing to want to be involved in research on that front. So what sorts of questions or big ideas sparked your research drive?
NEVILLE: Yeah, that’s actually an interesting story, as well. I only got into research because I was in the honors program in my computer science degree, and as part of the honors program, you have to do a research project [LAUGHTER].
ATALLA: It’s mandatory, yeah.
NEVILLE: It’s mandatory. And so when I talked to professors about what the research project should be, they actually said, “Well, you have to decide what topic you want to work on.”
And at that point, I was interested in data and I was interested in AI, and I thought a lot. I did a lot of reading. And what I decided that I wanted to investigate was how to do data mining on web data that was interconnected, and so that … when I decided that was the question I wanted to work on, I got pointed to a particular faculty member …
ATALLA: Nice.
NEVILLE: … who had just started working in this nascent field at the time that was called statistical relational learning. And we did my project in that, I published a paper at a workshop, and I was, kind of, hooked.
ATALLA: OK, yeah.
NEVILLE: So I hadn’t planned to go on to grad school, but that experience …
ATALLA: Yeah.
NEVILLE: … made me want to go on to grad school and continue.
ATALLA: Nice. What part of it do you think hooked you? Was it, like, the thrill of doing the research? Was it the environment of the conference and what academic publishing looks like?
NEVILLE: It was really the thrill and the process of doing research. So it was a long project. There were times that I felt like I was spinning my wheels. Things weren’t working. I would get, kind of, dejected. But my adviser would be, kind of, like, you know, supportive and positive, saying, “No, keep going. We’re learning something.” And then, and then we’d get to a point where we did learn something, and it was just the emotional thrill of that was, that was really what hooked me. The being able to, kind of, understand something that no one else understood yet …
ATALLA: Sure.
NEVILLE: … because it’s just at the frontier of what we know about things was, uh, it’s just, it’s like a drug almost, right? [LAUGHTER] So it’s kept me in research for this long, that same, that same … chasing after that same feeling.
ATALLA: Gotcha. Well, love it. Yeah, you joined Microsoft in 2021 from academia. And in fact, you’re still a professor, and you’ve been teaching and advising for 20 years. What motivated the shift to part of your professional life being research in industry, and how would you characterize the difference between industry research and academia research?
NEVILLE: My research, kind of, spans the spectrum from theory to application. When I did my first sabbatical after I got tenure, a lot of colleagues that I had at the … that were at the same point in their career went off into industry labs for their sabbatical and never came back to Purdue. I thought about which direction I’d want to go, either more theory or more applied, and I thought at that point in my career, it’d be better to explore the theory side because then I might actually go back to Purdue.
And so I went to the Simons Institute in Berkeley for my sabbatical, and that was very theoretical. It was a great experience, but it was kind of seamless to transition back to academia. On my second sabbatical, I went the other way, which was to come to MSR [Microsoft Research] and do a sabbatical here. And of course, being able to see how algorithms in theory, kind of, hit the—where the rubber hits the road with respect to how they behave in practice and real systems and with real users and real data, that’s hard to turn back from. And so that’s now why I’m still here.
ATALLA: Gotcha.
NEVILLE: So I think the difference between research in academia and industry depends on … really is affected by the target of where, where you’re aiming the research. And so I think fundamentally, it feels very similar, the questions you would ask in academia and industry, but in industry, your ability to apply it at scale in real systems on real data is really very different from academia, and in academia, I think you end up asking questions that are more abstractions that cover applications across a lot of different domains at once. And that’s how you get funding from places like DARPA [Defense Advanced Research Projects Agency] and NSF [National Science Foundation].
But in industry, it’s a little easier to just, you know, actually get your hands dirty and, and do it in the real systems. And then your, kind of, target is the products and the company’s interests. And so as … that sort of changes, maybe the types of questions that you would ask or investi