Wenhao Ding


Wenhao Ding

丁文浩


Ph.D. Candidate, Department of Mechanical Engineering, Carnegie Mellon University.

M.S., Machine Leaning Department, Carnegie Mellon University.

Github / Google Scholar / LinkedIn / CV / Email

I am a third-year Ph.D. student advised by Prof. Ding Zhao of Safe AI Lab. I am also co-advised by Prof. Bo Li of Secure Learning Lab. I received my Bachelor degree from Electronic Engineering, Tsinghua University.

I am an FPV drone flyer. Check my gallery.

My research goal is to build robust, generalizable, and interpretable autonomous systems that can truly understand the physical world. To provide learning-based algorithms with effective data, I focus on automatically generating tasks and environments used for data augmentation, adversarial training, invariant feature extraction, and multi-task learning. Specifically, I am interested in the following topics:

  • Adversarial Machine Learning. Improving robustness by training against semantic adversarial examples.
  • Zero-shot Generalization. Generalizating algorithms to unseen tasks by extracting structured features.
  • Causal Reinforcement Learning. Making generalizable decisions by discovering the underlying causality.
  • 2022/04 - We got into the Finalist of 2022 Qualcomm Innovation Fellowship.

    2022/03 - I will start my Applied Scientist Intern in Amazon Lab126.

    2022/02 - I gave a talk about scenario generation in Upenn hosted by Prof. Rahul Mangharam.

    2022/01 - We are holding the Season Depth Challenge in IROS 2022 and ICRA 2022.

    2022/01 - I gave a talk about scenario generation in Stanford SISL Lab.

    A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective

    Wenhao Ding, Chejian Xu, Mansur Arief, Haohong Lin, Bo Li, Ding Zhao
    Preprint arXiv:2202.02215

    arXiv / code / bibtex

    Semantically Adversarial Driving Scenario Generation with Explicit Knowledge Integration

    Wenhao Ding, Haohong Lin, Bo Li, Kim Ji Eun, Ding Zhao
    Preprint arXiv:2106.04066

    arXiv / code / bibtex

    Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems

    Mansur Arief, Yuanlu Bai, Wenhao Ding, Shengyi He, Zhiyuan Huang, Henry Lam, Ding Zhao
    Preprint arXiv:2111.02204

    arXiv / code / bibtex

    Context-Aware Safe Reinforcement Learning for Non-Stationary Environments

    Baiming Chen, Zuxin Liu, Jiacheng Zhu, Mengdi Xu, Wenhao Ding, Liang Li, Ding Zhao
    IEEE International Conference on Robotics and Automation (ICRA) 2021

    arXiv / code / bibtex

    Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation

    Wenhao Ding, Baiming Chen, Bo Li, Kim Ji Eun, Ding Zhao
    IEEE Robotics and Automation Letters (and ICRA 2021)

    arXiv / code / bibtex

    Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes

    Mengdi Xu, Wenhao Ding, Jiacheng Zhu, Zuxin Liu, Baiming Chen, Ding Zhao
    Neural Information Processing Systems (NeurIPS) 2020, Vancouver

    arXiv / code / bibtex

    Learning to Collide: An Adaptive Safety-Critical Scenarios Generating Method

    Wenhao Ding, Baiming Chen, Minjun Xu, Ding Zhao
    International Conference on Intelligent Robots and Systems (IROS) 2020, Las Vegas

    arXiv / code / video / bibtex

    CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios

    Wenhao Ding, Mengdi Xu, Ding Zhao
    International Conference on Robotics and Automation (ICRA) 2020, Paris

    arXiv / code / supplementary / bibtex

    Hierarchical Reinforcement Learning Framework towards Multi-agent Navigation

    Wenhao Ding, Shuaijun Li, Huihuan Qian
    IEEE International Conference on Robotics and Biomimetics (ROBIO) 2018, Malaysia

    arXiv / code / bibtex

    Conference Reviewer: ICML, ICLR, NeurIPS, CVPR, ECCV, ICCV, ICRA, IROS, ICME

    Journal Reviewer: TMLR, IEEE RA-L, IEEE Access, IEEE T-ITS, IEEE TII, IEEE MM

     

      Updated on April 7th, 2022

                   
          Wenhao Ding © 2018-2022