Mathematics taught me to reason rigorously about abstract and complex problems by emphasizing structure and invariance. Throughout my practical experience, I have always aimed to uncover underlying structures using mathematical frameworks and to scale solutions with computational tools, ensuring both elegance and practicality. I earned a B.S. in Mathematics from Haverford College, graduating Magna Cum Laude and receiving High Honors from the Mathematics Department. For my senior thesis under Professor Joshua Sabloff, I applied computational methods and machine learning to knot theory and low-dimensional topology, generating novel insights. Previously, I conducted research in Topological Data Analysis for signal processing and network structure and contributed to LLM projects, including the development of a lightweight generative grading system for evaluating textual and visual responses. Outside academia, I gained industry experience through both advisory and trading roles. I interned on the Deals M&A team at PwC. Afterward, I spent two summers in proprietary trading at Yuanta Securities, a leading market maker in Taiwan, as a quant research intern supporting the derivatives desk and ETF team. I am especially interested in quantitative research and systematic trading. I look forward to connecting virtually or in person!
