An Interview with Raghu Meka , Professor of Computer Science at UCLA, the 2025 IEEE CS 2025 W. Wallace McDowell Award Recipient, who isa leading researcher in theoretical computer science whose work spans complexity theory, pseudorandomness, learning theory, and combinatorics—advancing the mathematical foundations that underpin modern algorithms and machine learning. We connected with Prof.Meka to explore his impactful journey.
Your research in complexity theory and learning theory has garnered significant attention. What drives your interest in these foundational areas of computer science?
I’m drawn to theoretical computer science because it hits a rare sweet spot: clean, foundational questions that also connect to—and often pioneer—real systems. Take communication complexity: asking how little communication is needed to complete a task seems simple, but it opens deep links to analysis, combinatorics, number theory, algorithms, and information theory. That blend of elegance and impact is what keeps me hooked.
You’ve made notable contributions to additive combinatorics. How do these mathematical insights translate to practical applications in computer science?
While the exact results we obtained in additive combinatorics do not yet translate to practical applications, I strongly believe that the fundamental insights will eventually lead to deciphering fundamental algorithmic questions of practical importance. For instance, there is hope that the insights we developed in the context of additive combinatorics will lead to faster algorithms for finding cliques in hypergraphs, an algorithmic problem that has not seen any progress for several decades.
Receiving the 2025 W. Wallace McDowell Award is a testament to your impactful work. How do such recognitions influence your future research directions?
In an ideal world, awards should inspire you but not fundamentally change what you work on. That said, an honor like the McDowell Award does give me added confidence to dig deeper into the directions recognized here—and it helps me justify investing more time and energy, not just my own but also from students and collaborators, in the long-horizon problems we care about.
How do you balance deep theoretical work with the need for tangible applications in today’s technology-driven world?
That’s a significant challenge in any area of research. My philosophy is that it’s good to be guided by the need for tangible applications, but this shouldn’t dictate the choice of problems. Personally, I’m more driven by curiosity and the potential for elegance. Fortunately, in a field like theoretical computer science, many important problems are directly connected to applications. For work that isn’t immediately connected, history shows that if a concept is elegant and addresses a foundational issue, it will, more often than not, have an impact in the long run. I am confident in that pattern.
Your academic journey includes time at the Institute for Advanced Study and Microsoft Research. How have these experiences shaped your research philosophy?
IAS and Microsoft Research (MSR) were very different environments, and both were formative for my career and for how I think about research. I spent time at MSR as a student (two summers as an intern) and later a year as a researcher. Those were incredibly fun and productive years. The lab culture was exceptional: people were eager to share what they were working on—even with colleagues far outside their areas—and equally eager to hear about advances in algorithms. That atmosphere helped me appreciate the breadth of computer science beyond theory and the importance of being willing to cross boundaries.
The Institute for Advanced Study was a different kind of influence. Simply being in a place with such a storied history was inspiring, and the community leaned more heavily toward pure math and theoretical computer science. In addition to the great mentorship I received, the most lasting impact was the network of lifelong collaborations and friendships I formed there. IAS always has a large number of young scholars (postdoctoral researchers), and we all became very good friends who were willing to share everything from research ideas to professional advice to personal matters. Many of those relationships remain central to my life and work today.
In teaching complex theoretical concepts, what strategies do you employ to make them accessible and engaging to students?
When students encounter complex concepts, it is often difficult for them to appreciate the potential light at the end of the tunnel and understand why the initial complexity or drudgery is necessary. However, if they believe there will be a “payoff,” they are far more motivated to devote the effort needed to dig deep. With that in mind, my core strategy is to consistently highlight the “why” behind various concepts and theories, ensuring students always understand the value of what they’re learning.
Collaboration often plays a crucial role in research. Can you discuss a collaborative project that significantly advanced your work?
Almost all of my works have been collaborative. So it is hard to pick one to highlight.
What advice would you give to students interested in pursuing research in theoretical computer science?
In my view, success in research has a lot more to do with working on a problem that you love thinking about and in many cases running into people you love doing this with. And a lot of luck. One aspect that is perhaps less appreciated is the willingness to have a slow year or two in terms of visible productivity. During my PhD, I was lucky to have an environment which did not make me feel insecure even though I wasn’t publishing much and that has stayed with me since. I had my first major conference papers in my fifth year of PhD. I suspect most current students would have trouble with this, given the increasing pressure for early publication.
Dr. Raghu Meka’s journey exemplifies how deep theoretical insight, interdisciplinary rigor, and a commitment to foundational research can redefine the boundaries of computer science. His pioneering work in complexity theory, pseudorandomness, and the interplay between combinatorics and probability theory reflects the spirit of the IEEE CS W. Wallace McDowell Award in advancing computing through profound theoretical contributions that shape the future of algorithms, learning, and computational understanding far beyond the lab.
