TILOS Seminar with Adam Klivans
Title and abstract coming soon…
Adam R. Klivans, Professor of Computer Science at the University of Texas at Austin and Director of the NSF AI Institute for Foundations of Machine Learning (IFML), is a leading researcher in theoretical computer science whose work has profoundly shaped the foundations of modern machine learning. His research explores the complexity-theoretic limits of efficient learning, with major contributions to agnostic learning, noise-tolerant algorithms, and high-dimensional statistical inference, while also advancing the study of pseudorandomness and circuit complexity to uncover structural insights that inform the design of robust learning algorithms. By developing rigorous frameworks for understanding what can and cannot be learned efficiently, his work bridges computational complexity and machine learning, influencing both the theoretical landscape and the development of practical algorithms for real-world data. Dr. Klivans is a recipient of the NSF CAREER Award, has served on editorial boards for leading journals in theoretical computer science and machine learning, and has taken on program committee leadership roles at premier conferences such as COLT and FOCS, contributing both as a scholar and as a mentor to the next generation of researchers in machine learning and computational complexity.