معرفی
Nikita Zhivotovskiy is a tenure-track Assistant Professor in the Department of Statistics at the University of California, Berkeley. He previously held postdoctoral positions at ETH Zürich and Google Research, Zürich, and was affiliated with the Technion I.I.T. His academic background includes a PhD from the Moscow Institute of Physics and Technology, with affiliations during his studies at the Institute for Information Transmission Problems, Higher School of Economics, and Skoltech.
His research lies at the intersection of mathematical statistics, probability theory, and learning theory. Key areas include robust estimation, online learning, statistical learning theory, algorithmic stability, and high-dimensional statistics. His work emphasizes theoretical foundations of machine learning, with a focus on generalization, risk bounds, and learning under non-standard assumptions.
The recent publications reflect a strong trend in theoretical machine learning, particularly in understanding the limits and optimality of learning algorithms. Topics span PAC learning, online classification, private estimation, and clustering, often achieving dimension-free or high-probability guarantees. His work frequently appears in top venues such as NeurIPS, COLT, and FOCS, indicating significant impact in the field.
Scientific Awards:
- Best Paper Award at Conference on Learning Theory (COLT), 2020
Nikita Zhivotovskiy has served as a reviewer for leading journals including Annals of Statistics, Probability Theory and Related Fields, and IEEE Transactions on Information Theory, and as a senior program committee member for COLT and ALT. He has co-taught courses at ETH Zürich and has advised or collaborated with numerous researchers, though formal students are not listed. His research has been supported through academic and industrial collaborations, including at Google Research.
His work is embedded within the theoretical machine learning community, with active participation in workshops such as those at BIRS, and a growing body of work that bridges statistical theory and algorithmic design. While no formal lab is mentioned, his research group at UC Berkeley likely focuses on foundational aspects of learning and inference.
