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Michał Dereziński is an Assistant Professor of Computer Science and Engineering at the University of Michigan. Previously, he was a postdoctoral fellow in the Department of Statistics at the University of California, Berkeley, and a research fellow at the Simons Institute for the Theory of Computing. Michał obtained his Ph.D. in Computer Science from the University of California, Santa Cruz, where he received the Dissertation Award for his work on sampling methods in statistical learning. His current research focuses on the theoretical foundations of randomized algorithms, machine learning, and optimization, particularly in Randomized Numerical Linear Algebra. His contributions to the theoretical foundations of machine learning have been recognized with the Google ML Systems Junior Faculty Award. He has published numerous papers in leading conferences such as NeurIPS and SODA, and he is actively involved in teaching various courses in computer science.
University of Michigan • Ann Arbor, MI
Teaching courses and conducting research in theoretical computer science and machine learning.
Department of Electrical Engineering and Computer Science