Michał Dereziński

Michał Dereziński is an Assistant Professor of Computer Science and Engineering at the University of Michigan, affiliated with the College of Engineering. His research focuses on the theoretical foundations of randomized algorithms for numerical linear algebra, machine learning, optimization, and data science. Previously, he held postdoctoral positions at UC Berkeley (Department of Statistics) and the Simons Institute for the Theory of Computing, and completed his Ph.D. in Computer Science at UC Santa Cruz under Manfred Warmuth, alongside Master's degrees in Mathematics and Computer Science from the University of Warsaw.

He teaches courses such as Foundations of Computer Science (EECS 376), Algorithms for Data Science (EECS 474), and specialized RandNLA courses. His work emphasizes bridging theory and practice in RandNLA, with applications to ML and optimization. Key achievements include an NSF CAREER Award and a NeurIPS 2020 Best Paper Award for work on column subset selection.

Current advisees include Sachin Garg, Jiaming Yang, and Shabarish Chenakkod. His research explores topics like sketching algorithms, stochastic optimization, and random matrix theory, with publications in top venues such as NeurIPS, COLT, and SIAM journals. He actively contributes to the RandNLA community through tutorials and software development efforts like RandBLAS/RandLAPACK initiatives.