LinkedIn Profile How do you turn structured thinking into a measurable edge in financial markets? I’m someone who is drawn to this question. I am pursuing quantitative research roles, with interests across equities, fixed income, credit, and alternative data. I will be joining Carnegie Mellon’s MSCF program, building on my background in mathematics and data science from NYU Shanghai, with a focus on applying these foundations to empirical problems in finance. In prior internships, I worked across equity factor research and crypto portfolio construction. I improved and evaluated investment signals on large-scale financial datasets, focusing on improving robustness and out-of-sample performance through feature design and empirical validation. I also worked on portfolio allocation problems, developing strategies that balance return and risk under realistic constraints, with an emphasis on stability beyond in-sample results. Besides, as a research assistant at NYU Shanghai, I use SQL and Python to build and analyze large multi-dimensional datasets, studying structured patterns in behavioral data. Through sustained involvement in community service activities, I strengthened my organizational and communication skills in working with diverse groups. I’m always happy to connect virtually or in person to discuss markets and modeling, and how I can bring value to your team through my analytical perspective.
