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I'm a first-year Ph.D. student in Computer Science at the University of Texas, Austin, where I am fortunate to be advised by Kevin Tian. Previously, I completed my B.A. in Computer Science at Boston University. I work on data privacy and log-concave sampling. More broadly, I'm interested in problems at the intersection of theoretical computer science, high-dimensional statistics, probability theory, and machine learning. CV / Google Scholar / Twitter
News
2025.8 Started my PhD at UT Austin 2025.4 Attended ICLR 2025 in Singapore |
[email protected]
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Authors are ordered alphabetically unless authors are starred. Then, authors are in contribution order with starred authors contributing equally. Differentially Private Wasserstein BarycentersAnming Gu*, Sasidhar Kunapuli, Edward Chien, Mark Bun, Kristjan Greenewald In submission arXiv Mirror Mean-Field Langevin Dynamics Anming Gu, Juno Kim In submission arXiv Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics Anming Gu*, 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 ICLR 2025. arXiv Partially Observed Trajectory Inference using Optimal Transport and a Dynamics Prior Anming Gu*, Edward Chien, Kristjan Greenewald ICLR 2025. Preliminary version in OPT Workshop on Optimization for Machine Learning, 2024. arXiv / poster / code k-Mixup Regularization for Deep Learning via Optimal Transport Kristjan Greenewald*, Anming Gu, Mikhail Yurochkin, Justin Solomon, Edward Chien TMLR 2023. arXiv / code |