About
Peide Huang
I am a third-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 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, autonomous driving, and healthcare.
Email / CV / Google Scholar / Twitter / Github
News
- 04/2023: I was awarded a fellowship from the CMU Machine Learning Department.
- 01/2023: My coauthored paper Group Distributionally Robust RL is accepted by AISTATS 2023!
- 10/2022: I received Top Reviewer Award and Scholar Award from NeurIPS 2022 Committee!
- 09/2022: My first-author paper GRADIENT is accepted by NeurIPS 2022! Thanks to all the co-authors!
- 06/2022: Our work on accelerated policy evaluation in the presence of rare events is accepted by IROS 2022!
- 04/2022: My first conference paper is accepted by IJCAI 2022. Thank all the co-authors!
- 02/2022: I just passed my Ph.D. qualification exam!
Publications
Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao.
The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)
Scalable Safety-Critical Policy Evaluation with Accelerated Rare Event Sampling
Mengdi Xu, Peide Huang, Fengpei Li, Jiacheng Zhu, Xuewei Qi, Kentaro Oguchi, Zhiyuan Huang, Henry Lam, Ding Zhao
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022)
Abridged in ICLR 2021 Workshop in Security and Safety in Machine Learning Systems
Latent Goal Allocation for Multi-Agent Goal-Conditioned Self-Supervised Imitation Learning
Peide Huang*, Laixi Shi*, Rui Chen*
* 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, deep learning, and robotics
M.S.
Aug 2023 -- Present
Carnegie Mellon University, PA, USA
Specialization: machine learning
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
Internship
Machine Learning Research Intern
May 2023 -- Aug 2023
Robert Bosch LLC
Machine Learning Research Intern
Aug 2022 -- May 2023
Cleveland Clinic
Responsibility: proposed a novel multi-modal representation framework for complex cardiovascular Magnetic Resonance Imaging (cMRi).
System Engineer Intern
Aug 2018 -- June 2020
Flexiv Robotics Ltd.
Responsibility: developed a robot evaluation platform in Simulink and a communication protocal in C++ for fast prototyping.
Research Assistant
Jan 2017 -- June 2017
Agency for Science, Technology and Research, Singapore
Responsibility: designed a mobile robot that can change its morphology according to the environment constraints.
Academic Services
Reviewer
- NeurIPS, ICML, AISTATS, ICASSP, CVPR