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Adam Klivans is a professor in the Department of Computer Science at the University of Texas at Austin, where he leads research in machine learning and theoretical computer science. His research interests focus on Learning Theory, Computational Complexity, Pseudorandomness, Limit Theorems, and Gaussian Space. He also directs the NSF AI Institute Foundations Machine Learning and the Machine Learning Laboratory. Klivans serves on the editorial board of the Theory of Computing and the Machine Learning Journal, contributing significantly to the academic community. His work has garnered notable recognition, including the Teaching Excellence Award from the College of Natural Sciences in 2013 and the NSF CAREER Award from the National Science Foundation in 2007. With a dedication to advancing the field of machine learning, Klivans remains at the forefront of innovative research and education in computer science.
University of Texas at Austin • Austin, TX
Teaching and conducting research in machine learning and theoretical computer science.
General requirements for the Graduate School at UT Austin apply to all programs unless otherwise specified.