About
Peide Huang
I am a second-year Ph.D. student advised by Prof. Ding Zhao @ SafeAI Lab and co-advised by Prof. Fei Fang @ AI and Social Good Lab at Carnegie Mellon University. Prior to joining CMU, I received my Bachelor's degree from Nanyang Technological University, Singapore and Master's degree from Stanford University.
My research goal is to understand the interaction between the reinforcement learning agent and the tasks, with the objective to enable robust, safe, and explainable decision making. To achieve this goal, I leverage curriculum learning, representation learning, and game theory. I also tackle real-world applications in robotics and autonomous driving.
Email / CV / Google Scholar / Twitter / Github
News
- 04/2022: My first paper got accepted to IJCAI 2022. Thank all the co-authors!
- 02/2022: I just passed my Ph.D. qualification exam!
Publications
Group Distributionally Robust Reinforcement Learning
Mengdi Xu, Peide Huang, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao.
Preprint, under review
Abridged in ICRA 2022 the Fresh Perspectives on the Future of Autonomous Driving Workshop
Latent Goal Allocation for Multi-Agent Goal-Conditioned Self-Supervised Imitation Learning
Peide Huang*, Laixi Shi*, Rui Chen*
Accelerated Policy Evaluation: Learning Adversarial Environments with Adaptive Importance Sampling
Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao
Preprint, under review
Abridged in ICLR 2021 Workshop in Security and Safety in Machine Learning Systems
* indicates equal contribution
Curriculum Vitae
Click here to download my full CV.
Education
Ph.D.
Aug 2020 -- Present
Carnegie Mellon University, PA, USA
Advisior: Prof. Ding Zhao and Prof. Fei Fang
Specialization: reinforcement learning, multi-agent system
M.S.
Aug 2018 -- June 2020
Stanford University, CA, US
Specialization: robotics, control
B.E.
Aug 2014 -- June 2018
Nanyang Technological University, Singapore
Specialization: aeronautics and space engineering
Academic Services
Reviewer
- ICML 2022