Adam Klivans

Adam Klivans is a Professor at the University of Texas at Austin's Department of Computer Science and serves as Director of the Institute for Foundations of Machine Learning (IFML) and the Machine Learning Lab. His work bridges machine learning and theoretical computer science, focusing on learning theory, computational complexity, and Gaussian space analysis. He has made foundational contributions to robust learning, distribution shift, and neural network trainability.

Research Interests:

• Machine learning algorithms
• Computational complexity
• Robust learning under noise
• Distribution shift
• Gaussian space analysis
• Protein engineering

Selected Publications Analysis (2023-2025):

• Foundational TDS learning framework for distribution shift
• Polynomial-time algorithms for halfspace and ReLU regression
• Key results on SQ lower bounds for neural networks
• Advances in list-decodable regression and outlier-robust learning
• Novel approaches to protein stability prediction
• Applications to Ising models and diffusion models

Scientific Honors:

• NSF CAREER Award (2007)
• Microsoft Azure Data Science Initiative Award (2017)
• Best Student Paper COLT (2006)
• Best Paper Award at COLT (2024)
• Spotlight Presentations at NeurIPS (2019)
• Long-Term Participant Simons Institute (2018)

Academic Leadership:

• Director, Institute for Foundations of Machine Learning
• Director, UT Machine Learning Lab
• Editorial Board, Theory of Computing Journal
• Mentoring current students: Kostas Stavropoulos, Gautam Chandrasekaran, Kulin Shah
• Alumni advisees: Surbhi Goel, Pravesh Kothari, Sushrut Karmalkar