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I'm a first-year Ph.D. student in Computer Science at UT Austin, where I am fortunate to be advised by Kevin Tian. Previously, I completed my B.A. in Computer Science at Boston University. I primarily work on data privacy and logconcave sampling. More broadly, I'm interested in problems at the intersection of theoretical computer science, high-dimensional statistics, probability theory, and machine learning. I will be interning at A*STAR in Singapore during the summer of 2026, hosted by Atsushi Nitanda. Email / CV / Google Scholar / Twitter
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anminggu@cs.utexas.edu
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Authors are ordered alphabetically unless they are not. Functional Stochastic LocalizationAnming Gu, Bobby Shi, Kevin Tian In submission arXiv Mirror Mean-Field Langevin Dynamics Anming Gu, Juno Kim International Conference on Machine Learning (ICML), 2026. arXiv Differentially Private Wasserstein Barycenters Anming Gu*, Sasidhar Kunapuli, Mark Bun, Edward Chien, Kristjan Greenewald arXiv Compute-Optimal LLMs Provably Generalize Better with Scale Marc Anton Finzi*, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J Zico Kolter, Andrew Gordon Wilson International Conference on Learning Representations (ICLR), 2025. arXiv Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior Anming Gu*, Edward Chien, Kristjan Greenewald International Conference on Learning Representations (ICLR), 2025. Preliminary version in NeurIPS OPT workshop, 2024. arXiv / poster / code k-Mixup Regularization for Deep Learning via Optimal Transport Kristjan Greenewald*, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien Transactions on Machine Learning Research (TMLR), 2023. arXiv / code |