LinkedIn Profile I am interested in where modern AI meets rigorous mathematics and market discipline. I graduated cum laude from UCLA in 2026 with a B.S. in Applied Mathematics and Data Science Engineering. My goal is to pursue quantitative research and systematic trading roles where I can combine machine learning, stochastic thinking, rigorous implementation, and market intuition. My experience has taught me that the challenge in quantitative finance is not simply using more complex models, but building models that can survive noise, leakage risk, transaction costs, and changing regimes. At Tianhong Asset Management, I built a deep learning alpha research pipeline for China A-share return prediction, working across factor selection, time-series modeling, and IC evaluation. At Icarus Fund in New York, I worked on systematic intraday strategies, where backtesting, drawdowns, position sizing, and execution constraints shaped how I evaluated signals. Beyond internships, I have built projects in cross-sectional stock return prediction, transformer-based language modeling, reinforcement learning, and beta-oriented portfolio research. I bring strong Python and C++ implementation ability, machine learning experience, and a rigorous mathematical foundation in linear algebra, probability, and stochastic processes. I welcome conversations with researchers, traders, and investment teams exploring AI-driven, mathematically grounded approaches to financial markets.
