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Tian Li is an Assistant Professor at the University of Chicago, specializing in Computer Science and Data Science. His research centers on distributed optimization, federated learning, and trustworthy machine learning. He is particularly interested in designing, analyzing, and evaluating principled learning algorithms that account for practical constraints while addressing critical issues related to accuracy, scalability, trustworthiness, and their interplays. He earned his Ph.D. in Computer Science from Carnegie Mellon University. Prior to his tenure at CMU, Tian received undergraduate degrees in Computer Science and Economics from Peking University. His work has been recognized notably with a Paper Award at the ICLR Workshop on Secure Machine Learning Systems and he was invited to participate in the EECS Rising Stars Workshop. Tian has been acknowledged as a Rising Star in Machine Learning and Data Science by multiple institutions.
Department of Philosophy