Let's agree at the outset the world is a complicated place, and social scientists have exacting jobs when it comes to measuring civic phenomena with precision.
After all, even careful studies raise follow-up questions: How much do their findings apply in other settings? Do conclusions about politics in one country apply to other countries? If you're studying voters in a lopsided election, will your findings apply to voters in a close election? Those questions are all a natural part of the research process.
That's where Naoki Egami comes in. Egami is an MIT political scientist whose specialty is the methodology of research. He carefully scrutinizes, for one thing, what social scientists call "external validity," whether the results of particular studies apply more generally.
"I always say political methodology is the field where you ask questions as a political scientist, but then you solve them like an applied statistician or an applied computer scientist," Egami says. "You find out the underlying mathematical problems behind the empirical challenges people face, and solve them optimally."
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As it happens, Egami's interests range widely. Years ago, before the current artificial intelligence craze, he started studying what happens when AI tools are introduced into studies. How accurate are they? How can researchers account for AI tendencies? Focusing on these and other questions has helped Egami build a broad portfolio of research, win awards, and flourish in his career. All the while, he retains interest in basic questions about politics, as well as measuring things correctly.
"You need both perspectives," Egami says. "If you only think about technical statistical theories, you might not work on interesting empirical problems sometimes. But if you only think about problems, you won't really solve them optimally; you'll solve them in an ad-hoc way. So, you really want to have both lenses."
Egami joined MIT's Department of Political Science as an associate professor with tenure in 2025. He is also a faculty affiliate of the Statistics and Data Science Center at the Institute for Data, Systems, and Society (IDSS).
Workshopping his career
Almost anyone who likes their job has experienced some good fortune in finding it. Egami's case calls to mind those adages about luck being a mixture of preparation and opportunity.
Egami grew up in Tokyo and attended the University of Tokyo. He was good at math and physics, but he also liked political philosophy and was unsure how to combine his interests. One day, Egami attended a workshop about U.S. graduate school, which he thought was about MBA programs. Actually, it was about PhD programs, and included a political scientist talking about using math in the field, so Egami asked her a question.
"The miracle is: That workshop had 200 people in it, and after it was done, I was packing my stuff to go home, and the panelist, who was a PhD student, came down from the stage and found me," Egami recalls. "She asked, 'Are you the one who said you're interested in political science in the U.S., and likes math?'"
She invited Egami to what he thought would be another career workshop, the following week. Once again, he was mistaken.
"I showed up, and it was an academic seminar," Egami continues. "There were only 20 people there. It was 19 professors, and me, a first-year undergrad." Then a professor named Kosuke Imai, now at Harvard University, gave a talk about his own research on using statistics in the social sciences.
"I was super-excited and felt if I could do even 20 percent of that, it would be a dream," Egami says. "I talked to Kosuke and said, 'I want to do what you're doing.' He probably thought I was just a random person."
Egami, thus bolstered, started pursuing the goal of becoming a political scientist. He received his BA after spending a year as an exchange student at the University of Michigan, and applied to graduate schools in the U.S., landing at Princeton University - where Imai eventually became one of his advisors. Working with Imai, Rafaela Dancygier, Brandon Stewart, and others, Egami generated papers on methodological topics like external validity - and found substantial interest when he presented them.
"That was a case where the audience or market told me what I should really work on," Egami says. After earning his PhD from Princeton in 2020, he joined the faculty at Columbia University, moving to MIT five years later.
Enjoying the spirit of MIT
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One of the hallmarks of Egami's work is very close scrutiny of the factors that can influence the results found in empirical studies.
"In statistics, you talk about whether the people in the data are similar, meaning the population data," Egami says. "But in political science, there are a lot of differences in context."
Consider the question of how much political campaigns sway the minds of voters. Political scientists have sometimes received permission to conduct field experiments in active political campaigns. That's a significant step toward generating robust results. And yet, not all campaign settings are the same. Politicians may let researchers in when they expect to triumph, and the dynamics in those races might differ from close races.
"It's great to do field experiments, and that's usually where people are allowed to do research," Egami says. "It's where politicians know they can win. But most of the time, we're interested in the battlefield races, the politically competitive districts. And the logic and voter behaviors can be different in those cases."
Egami's job, on one level, is to spot such differences and make other researchers aware of them.
Meanwhile, he has also developed a strong interest in scrutinizing the tools of machine learning, as applied to the social sciences. This predates the elevated interested in AI generated by ChatGPT, starting in late 2022. Some of Egami's work explores how to systematically identify errors introduced by AI tools and then account for this issue when using AI in research.
"In the past, social science data is something we carefully collect and take a long time to really validate before we analyze it," Egami says. "But if the generation of data is changing. If people use AI to generate data at scale, it can have errors. So I was already thinking: You want to have statistical methods that take into account these errors, otherwise many of the analyses will not be able to be replicated. That's how I started to work on a lot of things about AI."
All of this has brought Egami recognition and honors in the field. Last year, he received the Emerging Scholar Award from the Society for Political Methodology. He has also been the recipient of best paper awards from the American Political Science Association's sections for political methodology (in 2019 and 2025), experimental research (in 2024), and political networks (in 2022). Earning awards in three subfields of the discipline speaks to Egami's scholarly versatility.
In his view, though, the work he does in different areas is ultimately aligned.
"All these things are in parallel," Egami says. "I'm trying to start a new research agenda every three to four years. That helps me learn new topics and be motivated."
Further motivation, he says, comes from being at MIT and liking the experience.
"I already knew MIT was an amazing place I would enjoy," Egami says. Even so, in his time at MIT, he says, he has gained even more appreciation for the "spirit of engineering," in the sense of working systematically on solutions to ongoing problems, among other things. In any case, Egami has found the Institute to be a stimulating and congenial place to do his work.
"People are really nice at MIT," says Egami, who has been teaching both undergraduate and graduate classes.
He adds: "The Department of Political Science is really high-functioning, people are intensive in terms of their work, but it's just genuinely nice people."
And, yes, that's one claim about the world Egami does not have to double-check.
