Professor Kolar’s research focuses on developing statistical and machine learning models for scientific discovery from high-dimensional, noisy data. His work combines efficient optimization techniques with theoretically grounded methods to create models that not only predict but also reveal underlying data-generating mechanisms. Unlike black-box models, his approaches prioritize interpretability, making them valuable across domains such as life sciences and social sciences. Professor Kolar’s recent work explores scalable optimization, including stochastic algorithms for constrained problems and distributed methods for federated learning, enabling reliable and interpretable machine learning systems at scale. Email
Prior to joining MBZUAI, Professor Kolar earned his Ph.D. in Machine Learning from Carnegie Mellon University in 2013. He is a professor in the Department of Data Sciences and Operations at the USC Marshall School of Business. Professor Kolar’s research focuses on high-dimensional statistical methods, probabilistic graphical models, and scalable optimization techniques, driven by the goal of uncovering meaningful and scientifically significant structures from observational data. In recognition of his outstanding contributions to these areas, he was awarded the 2024 Junior Leo Breiman Award. He currently serves as an associate editor for the Journal of Machine Learning Research, the Annals of Statistics, the Journal of Computational and Graphical Statistics, and the New England Journal of Statistics in Data Science.
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