Summary
Mengyao Huang is a quantitative researcher and data scientist with 8 years of cross-disciplinary experience spanning quantitative finance, marketing science, and applied mathematics, currently mentoring SSRP at UC Berkeley. She combines rigorous academic training (PhD candidate in Marketing Science at Haas; MS in Quantitative Finance from Michigan) with hands-on implementation in R/RStan, Python, C++ and MATLAB to build mixture models, matrix factorization, and recommendation systems. Her background includes applying advanced statistical methods—EM, Bayesian mixtures, causal forests—and practical data engineering for CRM and large-scale matrix completion projects. Prior roles in quantitative trading and market research sharpened her ability to translate mathematical models into profitable trading strategies and stock forecasts. Based in Berkeley, she brings a rare blend of theoretical depth in stochastic processes and PDE/optimization with pragmatic coding and visualization skills, and she shares that curiosity publicly through an active GitHub “workshop” ethos.
8 years of coding experience
2 years of employment as a software developer
Master of Science - MS, quantitative finance and risk management, Master of Science - MS, quantitative finance and risk management at University of Michigan
Doctorate Degree, Marketing Science, Doctorate Degree, Marketing Science at University of California, Berkeley, Haas School of Business
Bachelor's Degree, Finance, Bachelor's Degree, Finance at Dalian University of Technology
Chinese, English