LinkedIn Profile The average person makes hundreds of decisions every day, but only a few are significant. I am fascinated by those consequential moments—and by how data, probability, and financial models can help us distinguish them under uncertainty. I am a graduate student in Computational Finance with a strong interest in quantitative research, systematic trading, and the application of machine learning to financial markets. I am particularly drawn to problems where large, noisy datasets contain weak but potentially meaningful signals and where rigorous statistical analysis can turn those signals into actionable insights. Through my academic, research, and industry experiences, I have worked with quantitative investment strategies, machine learning models, and market microstructure. I have applied statistical techniques to evaluate market behavior, developed and tested factor-based stock selection strategies, and explored deep learning methods for modeling relationships between options activity and underlying stock returns. These experiences have strengthened my ability to deconstruct a financial question into data analysis, model development, and empirical evaluation that rendered additional insights over the market. My background at the intersection of statistics, machine learning, artificial intelligence, and finance provides me with distinct perspectives. Outside of finance, I enjoy playing a variety of sports, including baseball, tennis, and snowboarding, as well as real-time strategy games, which further reinforce my ability in estimating probability, strategic play, and decision-making under incomplete information. Let's connect virtually or in person, so I can demonstrate how I can find that significance for your trading and investment team.
