LinkedIn Profile My interest in quantitative finance began with a question that kept coming up in both my coursework and internships: how can we make disciplined decisions when markets are noisy, fast-moving, and never fully predictable? At New York University, where I studied Computer Science and Mathematics, I built a foundation in probability, statistics, algorithms, and modeling. During my internships at China Securities, Cinda Fund, and Atlas Capital Management, I became especially drawn to the process of turning messy real-world market data into clear, testable investment ideas, from realized volatility forecasting and alternative alpha factor construction to regime-switching models and systematic equity strategy backtesting. I am pursuing MSCF because I want to deepen the connection between rigorous quantitative analysis and real financial decision-making. The program’s blend of mathematics, computing, and finance matches how I hope to grow as a quantitative researcher: technically precise, data-driven, and attentive to how models behave in changing markets. Outside of finance, I enjoy photography, tennis, and board games. I am excited to bring my curiosity and passion to the MSCF community and to learn from classmates with different experiences and perspectives.
