Ming Zhou
Assistant Professor at Shanghai AI Laboratory
Xuhui District, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolMing Zhou is an Assistant Professor and research engineer with 10 years of experience blending telecommunications, embodied AI, and reinforcement learning research into practical systems. Holding a PhD from Shanghai Jiao Tong University in multi-agent reinforcement learning, game theory, and ML, he has worked across industry labs and startups—most recently at Shanghai AI Laboratory—focusing on embodied AI, humanoid robotics and RL. He combines front-end development skills with back-end systems work, exemplified by contributions to the open-source MAgent platform where he implemented novel maze generation and grid-world enhancements for many-agent experiments. As a self-employed project lead since 2020, he translates research ideas into applied prototypes and production-ready components for autonomous systems. Based in Shanghai, he pairs deep theoretical grounding with hands-on engineering, often tackling the messy integration problems between simulation, control and large-scale agent environments.
10 years of coding experience
1 year of employment as a software developer
Bachelor's degree, Computer Science, GPA3.5/4.0, Bachelor's degree, Computer Science, GPA3.5/4.0 at Sichuan UniversityDoctor of Philosophy - PhD, Reinforcement Learning, Machine Learning and Game Theory, Doctor of Philosophy - PhD, Reinforcement Learning, Machine Learning and Game Theory at Shanghai Jiao Tong University
English, Chinese
Github Skills (9)
multi-agent10
c-language10
cprogramming-language10
reinforcement-learning10
deep-learning9
algorithms8
data-structures8
algorithm8
data-structure8
Programming languages (5)
Github contributions (5)
geek-ai/MAgent
Nov 2017 - Dec 2017
A Platform for Many-agent Reinforcement Learning
Role in this project:
Back-end Developer
Back-end DeveloperContributions:19 commits, 2 pushes, 3 branches in 21 days
Contributions summary:Ming primarily contributed to the back-end of the project, focusing on adding new maze generation options for the multi-agent reinforcement learning platform. They modified the `Map.cc`, `GridWorld.cc`, and `Map.h` files to incorporate the maze generation logic. Their changes included implementing functions for generating random mazes, adding walls, and modifying the grid world environment to utilize the new maze configurations.
agentdeep-learningreinforcement-learningreinforcement-learning-agentdeep-reinforcement-learning
sjtu-marl/malib
May 2021 - Jan 2023
A parallel framework for population-based multi-agent reinforcement learning.
Contributions:9 reviews, 305 commits, 47 PRs in 1 year 8 months
raypythonmultiagentgamesagent
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