Pasin Manurangsi received his Ph.D. from the University of California, Berkeley in 2019; his advisors were Prasad Raghavendra and Luca Trevisan. His thesis was on the intersection between hardness of approximation and fine-grained complexity. Prior to that, he received bachelor's and master's degrees from MIT, where he was advised by Dana Moshkovitz. He is currently a research scientist at Google Research, primarily working on theoretical and practical aspects of algorithmic privacy.

Preetum Nakkiran is a postdoctoral researcher at the University of California, San Diego. He is hosted by Mikhail Belkin, and part of the NSF/Simons Collaboration on the Theoretical Foundations of Deep Learning. He currently works on scientific aspects of machine learning, trying to understand when and why deep learning works. He obtained his Ph.D. in 2021 from Harvard, advised by Madhu Sudan and Boaz Barak. He did his undergraduate work in EECS at the University of California, Berkeley.

Luca Trevisan is a professor of computer science at Bocconi University. Luca received his Ph.D. from the Sapienza University of Rome, advised Pierluigi Crescenzi. After graduating, Luca was a postdoc at MIT and at DIMACS, and he was on the faculty of Columbia University, U.C. Berkeley, and Stanford, before returning to Berkeley in 2014 and, at long last, returning to Italy in 2019.

Luca's research is in theoretical computer science, with a focus on computational complexity, on the analysis of algorithms, on the foundations of cryptography, and on topics at the intersection of theoretical computer science and pure mathematics.

Luca received the STOC'97 Danny Lewin (best student paper) award, the 2000 Oberwolfach Prize, a 2000 Sloan Fellowship and an NSF CAREER Award. He was an invited speaker at the 2006 International Congress of Mathematicians. In 2019, he received an ERC Advanced Grant.