Tian Li
I am an Assistant Professor at the Computer Science Department and the Data Science Institute at the University of Chicago.
My research generally centers around large-scale machine learning and optimization. Specific topics include:
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Efficient distributed training with applications to LLM optimization and federated learning (FedProx, FedDANE, Ditto, TailOPT)
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Risk-averse or risk-seeking learning with applications to fairness and robustness (q-FFL, TERM)
I am particularly interested in designing, analyzing, and evaluating principled learning algorithms, modeling practical assumptions (e.g., communication constraints and heterogeneity) to address the above issues, as well as their interplays.
I am always looking for strong and motivated undergraduate and graduate students and postdocs. For Ph.D. applicants, please apply through the CS PhD program and/or the DS PhD program, and mention my name in your application(s).