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Tian Li is an Assistant Professor in the Department of Computer Science at the University of Chicago, focusing on data science and machine learning. His research includes distributed optimization, federated learning, and trustworthy machine learning. He specializes in designing, analyzing, and evaluating principled learning algorithms that account for practical constraints, addressing issues related to accuracy, scalability, and trustworthiness. Tian received his Ph.D. in Computer Science from Carnegie Mellon University. Prior to that, he earned undergraduate degrees in Computer Science and Economics from Peking University. He has received accolades for his research efforts, including the Paper Award at the ICLR Workshop on Secure Machine Learning Systems, and he has been invited to participate in high-profile workshops such as the EECS Rising Stars Workshop. His accomplishments also include recognition as a Rising Star in Machine Learning and Data Science at multiple institutions. Tian is committed to applications of computer algorithms that enhance data-centric models, predictions, and decisions.
Department of Philosophy