Summary
Tianwei Ni is a PhD candidate at Mila specializing in history-dependent reinforcement learning, with nine years of research experience across top AI labs and academia. He develops memory-aware agents and representations that improve adaptability, drawing on internships at AWS (working on LLM reasoning for agents) and AI2’s embodied AI group. His background includes graduate work and teaching at Carnegie Mellon and visiting research at Stanford, giving him strong foundations in both theory and applied RL. Tianwei’s trajectory spans human-agent teaming, imitation learning, and medical vision, indicating a willingness to cross domains to test ideas. Actively positioning for research scientist roles in 2026, he blends deep academic rigor with hands-on systems experience in deploying agent and LLM reasoning prototypes. His work suggests a rare combination of long-horizon memory modeling and practical engineering on cutting-edge agent architectures.
9 years of coding experience
2 years of employment as a software developer
High School Diploma (Honorary Graduate), High School Diploma (Honorary Graduate) at Shanghai High School
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Université de Montréal
Bachelor’s Degree, Computer Science, Bachelor’s Degree, Computer Science at Peking University
Master's degree, Machine Learning, Master's degree, Machine Learning at Carnegie Mellon University
No degree, Visiting Student, No degree, Visiting Student at Stanford University
Chinese, wu chinese, English