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Adam Klivans is a Professor at the University of Texas at Austin in the Department of Computer Science. He specializes in the fields of machine learning and theoretical computer science, with particular emphasis on learning theory, computational complexity, pseudorandomness, limit theorems, and Gaussian space. Klivans serves on the editorial board of the Theory of Computing and the Machine Learning Journal. Furthermore, he has participated in several prestigious programs and initiatives, including being a member of the Institute for Advanced Study's School of Mathematics and engaging in spotlight presentations at NeurIPS 2019. He has received numerous awards throughout his career, including the NSF Career Award and the Microsoft Azure Data Science Initiative Award. His research contributions are recognized in the computer science community, and he has a strong commitment to teaching excellence.
University of Texas at Austin • Austin, TX
Teaching and conducting research in the fields of machine learning and theoretical computer science.
General requirements for the Graduate School at UT Austin apply to all programs unless otherwise specified.