Summary
Tan Nguyen is an Assistant Professor of Mathematics at the National University of Singapore and founder of the Mathematical AI (MAI) Group, where his research bridges deep learning, optimization, differential equations, and statistics to build robust, interpretable, and efficient AI. With a Ph.D. in Machine Learning from Rice University and a postdoc at UCLA working with Stanley Osher, he blends rigorous theory with practical impact demonstrated by internships at Amazon AI and NVIDIA Research. His work emphasizes principled approaches—optimization, differential-equation modeling, and statistical foundations—to tackle interpretability, robustness, and scalability in modern ML. A recipient of prestigious awards including an NSF Graduate Research Fellowship and the CRA CIFellows postdoctoral fellowship, he also organized the first ICLR workshop on integrating deep models with differential equations. Based in Singapore, he maintains active industry ties (including a Qualcomm technical consultancy) while mentoring students to push “DeepLEAD” toward RISE AI: Robust, Interpretable, Scalable, and Efficient systems. Notably, his trajectory spans hands-on engineering (from VBA tools for industry to AFM experiments as an undergraduate) to cutting-edge mathematical AI research, reflecting rare breadth across theory and practice.
10 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Machine Learning, Doctor of Philosophy (Ph.D.), Machine Learning at Rice University
Associate of Science (A.S.), Engineering, 4.0/4.0, Associate of Science (A.S.), Engineering, 4.0/4.0 at Houston Community College
English, Vietnamese