Summary
Shengpu Tang is an Assistant Professor and machine learning researcher with a decade of experience developing computational methods that extract knowledge from sequential experiences, with a strong focus on healthcare applications. His work spans reinforcement learning, deep learning, time-series modeling, transfer learning, and fairness, aiming to translate clinical use cases into broadly relevant AI/ML innovations. Trained at the University of Michigan (MS, PhD) and experienced as a research intern at Microsoft, he blends rigorous academic research with practical system-building and mentoring experience in data-intensive health projects. He has taught and designed machine learning coursework, mentored summer institutes on messy clinical data, and contributed to applied risk-stratification efforts, highlighting his ability to move models from theory toward real-world impact.
10 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science and Engineering, Doctor of Philosophy - PhD Computer Science and Engineering at University of Michigan - Rackham Graduate School
GCE Advanced Level Certificate, GCE Advanced Level Certificate at Hwa Chong Institution
Bachelor's degree Computer Science, Bachelor's degree Computer Science at University of Michigan College of Engineering
Dual enrollment, Dual enrollment at National University of Singapore
English, Chinese, Japanese