Summary
Archie Huang is an Assistant Professor and researcher with a decade of experience at the intersection of civil engineering, informatics, and data-driven mobility systems. He specializes in physics-informed deep learning, traffic state estimation and forecasting, fleet rebalancing for shared mobility, and simulation of connected and autonomous vehicles, translating theoretical models into practical tools for transportation planning. His trajectory spans PhD research and postdoctoral work focused on PIDL and uncertainty quantification, teaching novel courses on nonlocal traffic dynamics and signal-free autonomous intersections, and developing stochastic models of social contagion. Based in Montreal, he blends rigorous academic training from UCF, NYU, and Tsinghua with applied research that addresses operational challenges in real-world mobility systems. Notably, his work often embeds physical laws into neural models to accelerate convergence and improve interpretability—bridging domain knowledge and machine learning in ways that reduce data needs while boosting robustness.
10 years of coding experience
7 years of employment as a software developer
Master of Science (M.S.), Informatics, Master of Science (M.S.), Informatics at New York University
Bachelor's degree, Electrical and Electronics Engineering, Bachelor's degree, Electrical and Electronics Engineering at Tsinghua University
Doctor of Philosophy - PhD, Civil Engineering, Doctor of Philosophy - PhD, Civil Engineering at University of Central Florida