Leonard J. Schulman

Leonard J. Schulman is a Professor of Computer Science at the California Institute of Technology (Caltech), affiliated with the Division of Engineering and Applied Science. He holds a B.Sc. (1988) and Ph.D. (1992) in Applied Mathematics from MIT. His research spans algorithms, coding theory, communication protocols, combinatorics, probability, quantum computation, and causal inference. He has directed the Caltech Center for the Mathematics of Information and served as Editor-in-Chief of the SIAM Journal on Computing.

His academic roles include Graduate Option Representative for Computer Science and leadership in interdisciplinary initiatives involving quantum computing, information theory, and complexity science. Schulman has held visiting positions at UC Berkeley, the Weizmann Institute, and Georgia Tech.

Research interests emphasize causal inference, non-reversible Markovian dynamics, and algorithmic foundations. Notable awards include the FOCS Test of Time Award (2022), UAI Best Paper (2016), and SIAM Fellowship. His work integrates theoretical computer science with applications in economics, biology, and physics.

Key contributions include tree code constructions, mixture model identification, and quantum algorithms. Active in education, he teaches advanced courses like CS 150b (Probability and Algorithms). Collaborations span Caltech’s IQIM, CMI, and CMS groups, with postdocs and students advancing interdisciplinary research.