I'm a PhD student in the Department of Applied Mathematics and Statistics at Johns Hopkins University, working on the theoretical foundations of AI, using tools from probability theory and optimization to understand modern large language model training and alignment.
Research
- > Paper Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in RLVR
- > Paper POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
- > Paper Understanding Inverse Reinforcement Learning under Overparameterization
- > Paper Improved Rates of Differentially Private Nonconvex–Strongly Concave Minimax Optimization via Gradient Differences
- > WIP Learning Optimal Robust Policies under Observational Data with Causal Transport
Industry Experience
- > Intern Research Intern, Mitsubishi Electric Research Laboratories (MERL)
Awards
- > 🏆 ASA Nonparametric Statistics Section Best Student Paper Award
- > 🏆 ICCOPT 2025 Student Grant
Teaching
- > TA Time Series Analysis
- > TA Data Science Methods for Large-Scale Graphs
- > TA Investment Science
- > TA Ordinary Differential Equations
- > TA Optimization
Education
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2024 — now
PhD, Applied Mathematics & Statistics
Johns Hopkins University
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2020 — 2024
BSc, Mathematics & Applied Mathematics (First Class Honors)
The Chinese University of Hong Kong, Shenzhen
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Apr — Aug 2023
Exchange, Mathematics
Technical University of Munich
last updated · 2026-06-03