What first drew me to quantitative research was not the models themselves, but the moment they stop working and what that reveals about the system underneath. My foundation in mathematics and data science at NYU Shanghai trained me to reason carefully under uncertainty. Through internships spanning equity alpha research, market timing, and fixed-income attribution, I developed the discipline of moving from model failure to structural diagnosis, whether rebuilding a factor specification or uncovering signals in a thin and underexplored market. Research at Alipay's investor behavior lab further broadened my perspective, demonstrating how behavioral patterns can be identified and measured in large-scale financial data through rigorous quasi-experimental design. Now at Carnegie Mellon's MSCF program, I am building on that foundation toward a career in quantitative research, with particular interest in roles where modeling discipline, empirical rigor, and market intuition converge to generate systematic edge. I would welcome the opportunity to connect virtually or in person to discuss how my background can contribute to your team.
