Summary
Sheng-tao Yang is a research scientist at Meta with a decade of experience bridging mathematical statistics and machine learning, grounded in a Ph.D. in Industrial Engineering from Georgia Tech and prior doctoral work in Statistics. He specializes in high-dimensional data analysis, feature selection, and clustering, and has hands-on experience applying AutoML pipelines to real-world problems like traffic and blood pressure datasets. Sheng-tao combines strong theoretical training with practical algorithm and software development, having taught advanced statistics and data mining courses to large cohorts and designed rigorous evaluation pipelines. His background as an educator—from high school math teacher to graduate teaching assistant—gives him a talent for translating complex methods into clear, usable solutions. Based in Bellevue, WA, he is driven by creating innovative, reproducible tools that push statistical methodology toward real-world impact. An often-overlooked strength is his early-career cross-disciplinary breadth, including music instruction, which reflects patience and a knack for structured practice.
10 years of coding experience
1 year of employment as a software developer
Bachelor of Science - BS, Applied Mathematics, Minor in Mathematics education, Bachelor of Science - BS, Applied Mathematics, Minor in Mathematics education at National Chiao Tung University
Doctor of Philosophy - PhD, Industrial Engineering, Minor in Machine Learning, Doctor of Philosophy - PhD, Industrial Engineering, Minor in Machine Learning at Georgia Institute of Technology
Doctor of Philosophy - PhD, Statistics, Doctor of Philosophy - PhD, Statistics at National Tsing Hua University
Chinese, English, German