LinkedIn Profile I studied Applied Mathematics, Statistics, and Economics at the University of Connecticut, where I developed a strong foundation in probability, stochastic processes, statistical modeling, and data-driven decision-making. My academic background and MSCF coursework give me the technical tools needed for quantitative finance, while my experience in machine learning and market research has shaped how I approach real-world financial problems. I am especially interested in quantitative research, systematic trading, and financial modeling. I hope to apply mathematical and statistical methods to understand market behavior, build testable investment signals, and improve decision-making under uncertainty. I previously applied this interest through internships in securities and trust companies, where I worked with Chinese equity and futures data, built predictive features, evaluated machine learning models, and analyzed backtest results. In the Jane Street Kaggle competition, my team earned a Silver Medal in the top 2%, which strengthened my ability to collaborate, validate models, and work carefully with noisy financial data. My background across mathematics, statistics, economics, and international financial markets gives me a broad perspective on quantitative finance. I look forward to continuing to develop my skills and applying them to challenging problems in quantitative finance.
