Summary
Guowei Yang is a machine learning engineer and environmental scientist combining eight years of practical experience with graduate training at Columbia Engineering and a Sino-Canadian dual background from China University of Geosciences and the University of Waterloo. He has applied ML models like random forest and CNNs to estimate biogenic carbon stocks in coastal wetlands and is now adapting bespoke pollutant dispersion and health-impact models for a large-scale GIS study of over 3,600 Chinese power plants. His work bridges rigorous field-focused data collection, geospatial analysis, and ML-driven modeling, demonstrating both domain depth and methodological versatility. A visiting researcher at Xiamen University, he has hands-on experience curating complex geospatial and emissions datasets and translating those into actionable environmental insights. Guowei aims to translate this technical expertise into strategic analytics roles at top consulting firms while advancing sustainable, green-energy solutions. Fluent in cross-cultural research environments, he brings a rare combination of environmental engineering rigor and practical data-science execution.
8 years of coding experience
Bachelor of Engineering - BE, water resources and engineering, Distinction, Bachelor of Engineering - BE, water resources and engineering, Distinction at 中国地质大学(武汉)
Bachelor of Science - BS, environmental science, Distinction, Bachelor of Science - BS, environmental science, Distinction at 加拿大滑铁卢大学
Master of Engineering - MEng, Earth and environmental engineering, Master of Engineering - MEng, Earth and environmental engineering at Columbia Engineering
Chinese, English