LinkedIn Profile I believe quantitative research thrives on restless curiosity: asking better questions, exploring cutting-edge tools, and conducting more rigorous backtesting. My background combines financial research, machine learning, and LLMs. I am especially interested in how LLMs can support signal discovery and refinement, as well as how they can help build pipelines that turn financial text—news, narratives, and earnings calls—into structured insights. I explored both directions through my research assistant work and my quantitative research internship. Beyond LLMs, I have also applied classical linear models and machine learning methods in coursework and competitions to support data-driven decision-making. I enjoy mixing market intuition with data to make financial questions testable. As an MS in Computational Finance student at Carnegie Mellon University, I am exploring opportunities across quantitative research, trading, and asset management. Please feel free to reach out if you are interested in data-driven market research, alpha research, or LLM applications in finance. I am available to meet on Zoom or in person to discuss how I could contribute to your team from day one—let’s connect!
