Summary
Yimeng Ren is a data scientist and statistician completing a PhD in Statistics at Fudan University and transitioning to a postdoctoral fellowship at HKUST focused on network data analysis, spatial econometrics, and high-dimensional statistical learning. With eight years of experience spanning academia, government research, and industry internships, Yimeng has applied rigorous statistical modeling to problems from grid carbon-emission mapping for policy reports to teacher-evaluation systems still used in operations. He combines strong theoretical training with practical data engineering—modularizing reproducible pipelines and visualizing geospatial emissions and economic trends—and has taught mathematical statistics at both Renmin University and Fudan. Comfortable bridging applied and theoretical work, he seeks collaborative projects that push methodological boundaries in networked and spatial data. Notably, his background includes a University of Minnesota summer project on neural-network-based matrix decomposition, reflecting an early interest in marrying modern ML with statistical foundations.
8 years of coding experience
3 years of employment as a software developer
Summer Project Neural Network Modelling for Matrix Decomposition, Summer Project Neural Network Modelling for Matrix Decomposition at University of Minnesota
Doctor of Philosophy - PhD Statistics, Doctor of Philosophy - PhD Statistics at Fudan University
Master of Science - MS Applied Statistics, Master of Science - MS Applied Statistics at Renmin University of China
English