Summary
Tianhong Tan is a PhD-level researcher and engineer specializing in scientific machine learning and Bayesian optimization to drive autonomous discovery in chemical and materials systems. With a decade of experience spanning graduate research and teaching roles at UW–Madison, Cornell, and Ohio State, Tianhong integrates data-driven models, uncertainty-aware optimization, and experiment-simulation loops to accelerate exploration of complex design spaces. Their work has been applied to digital soil mapping, peptide and protein design, automated molecular dynamics, and self-driving labs, demonstrating a rare blend of computational rigor and experimental integration. As a graduate teaching assistant and guest lecturer, they also translate advanced topics—like neural network uncertainty quantification—into actionable learning for students. Based in Madison, Wisconsin, Tianhong pairs deep chemical engineering training with practical lab automation know-how, often bridging academic research and industrial modeling experience from internships at Solvay/Syensqo. Unexpectedly, their portfolio spans scales from environmental mapping to molecular design, showing versatility in applying the same ML+Bayes toolkit across disparate domains.
10 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD, Chemical and Biological Engineering, Doctor of Philosophy - PhD, Chemical and Biological Engineering at University of Wisconsin-Madison
Master of Science - MS, Chemical and Biomolecular Engineering, Master of Science - MS, Chemical and Biomolecular Engineering at Cornell University
Bachelor of Engineering - BE, Chemical Engineering, Bachelor of Engineering - BE, Chemical Engineering at East China University of Science and Technology
Doctor of Philosophy - PhD, Chemical and Biomolecular Engineering, Doctor of Philosophy - PhD, Chemical and Biomolecular Engineering at The Ohio State University
Chinese, English