LinkedIn Profile I triple-majored in Statistics, Applied Mathematics, and Economics at the University of Connecticut, where I developed a strong foundation in quantitative methods and an interest in understanding financial markets through mathematical and statistical modeling. Through three quantitative research internships, I gained experience in options volatility research, systematic signal development, and financial modeling. My work included forecasting realized volatility, developing and backtesting trading strategies, and analyzing options PnL and risk exposures. These experiences helped me understand the importance of connecting statistical research with practical trading decisions, particularly when evaluating how models perform in real market conditions. Beyond my internships, I earned a Silver Medal in Jane Street's Real-Time Market Data Forecasting competition on Kaggle, which further developed my skills in machine learning, financial data analysis, and model validation. I am particularly interested in quantitative research, systematic trading, and global macro markets, with a focus on volatility and derivatives. What interests me most is understanding how markets behave, identifying patterns in noisy financial data, and translating research into actionable trading strategies. Looking ahead, I hope to pursue a career that combines rigorous quantitative analysis with market intuition and investment decision-making.
