Leonard J. Schulman

Leonard J. Schulman is a Professor of Computer Science at the California Institute of Technology (Caltech), where he has been on the faculty since 2000. He is affiliated with the Caltech Center for the Mathematics of Information (which he directed from 2003 to 2017) and the Institute for Quantum Information and Matter. His academic appointments have included positions at UC Berkeley, the Weizmann Institute of Science, the Georgia Institute of Technology, and the Mathematical Sciences Research Institute.

Schulman received his BSc in Mathematics in 1988 and his PhD in Applied Mathematics in 1992, both from the Massachusetts Institute of Technology (MIT).

Schulman's research spans several overlapping areas in theoretical computer science and applied mathematics. His work focuses on algorithms and communication protocols, combinatorics and probability, coding and information theory, and quantum computation. More recently, his research has expanded into causal inference and machine learning, particularly in the areas of mixture models, causal discovery, and structure learning. His approach combines deep theoretical insights with practical applications across multiple domains.

An analysis of Schulman's recent publications (2019-2025) reveals a strong focus on causal inference and machine learning, particularly in the areas of mixture models, causal discovery, and structure learning. His work bridges theoretical computer science with statistical learning, often developing novel algorithms with provable guarantees. He has also maintained his foundational work in coding theory, algorithms, and quantum computation, demonstrating remarkable breadth across theoretical computer science.

• IEEE Schelkunoff Prize (2004)
• ACM Notable Paper (2012)
• UAI Best Paper Award (2016)
• FOCS Test of Time Award (2022)
• S. A. Schelkunoff Transactions Prize Paper Award (2004)
• SIAM Fellow
• NSF CAREER award
• NSF mathematical sciences postdoctoral fellowship
• MIT Bucsela prize in mathematics

Schulman has advised numerous PhD students and postdoctoral researchers who have gone on to successful careers in academia and industry. His former students and postdocs include notable researchers such as Ashwin Nayak, Yaoyun Shi, Sean Hallgren, Jie Gao, and Michael Langberg. He served as Editor-in-Chief of the SIAM Journal on Computing from 2013 through 2018 and has been on the editorial boards of several prestigious journals including the Journal of the ACM, ACM Transactions on Algorithms, and SIAM Journal on Discrete Mathematics.

Schulman directs the Caltech Center for the Mathematics of Information, a research center focused on the mathematical foundations of information processing, communication, and computation. His work often involves interdisciplinary collaborations across computer science, mathematics, physics, and economics.