About

Mingyang is a first-year Ph.D. student in the EECS Department at MIT, focusing on developing algorithms for large-scale games, particularly those involving imperfect information such as Stratego and Texas Hold’em. During his first year, he completed four research papers, serving as the first author on three. These papers explored advancements in algorithms for deep learning in imperfect information games, developed a highly efficient library for game-solving, proposed a new metric for repeated games, and enhanced reinforcement learning with human feedback for training large language models.

Education

MIT

Ph.D. , Computer Science

Class of 2025

Awards & Publications