Mark Sellke
Mark Sellke is an Assistant Professor of Statistics at Harvard University and a researcher at OpenAI. He works at the interface of probability, mathematical physics, theoretical computer science, and statistics, with a focus on computational barriers and algorithmic thresholds in random complex systems including spin glasses and machine learning theory. He received his PhD in mathematics from Stanford University in 2022, advised by Andrea Montanari and Sébastien Bubeck, and his work has been recognized with best paper awards at SODA 2020 and NeurIPS 2021, a Sloan Research Fellowship, the Bernoulli New Researcher Award, and the Rollo Davidson Prize.
“a couple of years ago, like, proving the result was, like, so hard that kind of the other stuff was just kind of coming along for the ride... And, yeah, now this kind of, what was the main bottleneck before is kind of much less of a bottleneck.”
Source→AI-extracted from podcast / newsletter / paper summaries. May contain errors.