LinkedIn Profile Eighteen years of performance piano training taught me two things: to embrace pressure, and to sweat every detail. Quantitative research rewards the same instincts. I hold a B.S. in Honors Mathematics and Data Science from NYU Shanghai. Beyond coursework in statistical inference, numerical analysis and probabilistic machine learning, I dove into research at NYU's Courant Institute, working across statistical physics, sampling, rare-event simulation and natural language processing. That blend of rigorous theory and hands-on research taught me to turn pattern recognition into models that are both principled and efficient. Eager to put theory into practice, I pioneered a market-level variance risk premium factor for Chinese A-shares at NYU Shanghai's Volatility Institute, estimating exposures across 4,777 stocks via Fama-MacBeth and attributing a significant negative premium to retail dominance and thin hedging. Interning at Heji Private Fund Management, I engineered intraday factors on high-frequency data and fine-tuned a minute-level LightGBM model to 61% out-of-sample accuracy, improving Sharpe 14% after transaction costs. I aim to combine rigorous factor research, machine learning, and market intuition to generate alpha and sharpen trading decisions. I'd be glad to connect and discuss how I can contribute.
