Wenhao Ding


Wenhao Ding

丁文浩


Ph.D. Candidate, Carnegie Mellon University.

M.S. in Machine Learning, Carnegie Mellon University.

Github / Google Scholar / LinkedIn / CV (3/2023) / Email

I am a fourth-year Ph.D. candidate of Safe AI Lab, co-advised by Prof. Ding Zhao and Prof. Bo Li. I received my Bachelor degree from Electronic Engineering, Tsinghua University.

I am an FPV drone flyer. Check my gallery.

Research Highlights

My research goal is to build robust, generalizable, and interpretable autonomous systems that can truly understand the physical world. Specifically, I am interested in the following topics:

  • Intelligent Digital Twin. Generating critical scenarios for virtual autonomy development.
  • Causal Reinforcement Learning. Making generalizable decisions by discovering the underlying causality.
  • Adversarial Machine Learning. Improving robustness by training against semantic adversarial examples.

    • News & Updates

    2023/03 - We are hosting the Secure and Safe Autonomous Driving (SSAD) Workshop and Challenge at CVPR 2023!

    2023/01 - One paper about embodied AI accepted to ICRA 2023.

    2022/12 - My research is covered by CMU Engineering News Driving autonomy into the metaverse!

    2022/10 - Glad to present our work about causal discovery in Wayve!

    2022/09 - Two papers got accepted to NeurIPS 2022. See you in New Orleans!

    2022/09 - One paper about using causal graph to generate safety-critical scenarios got accepted to CoRL 2022.

    2022/08 - Our proposal "Safety-critical Scenarios Generation and Generalization for Autonomous Driving" wins 2022 Qualcomm Innovation Fellowship, North America.

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

    Selected Publications

    Bayesian Reparameterization of Reward-Conditioned Reinforcement Learning with Energy-based Models

    Wenhao Ding*, Tong Che*, Ding Zhao, Marco Pavone
    Under Review

    arXiv / code / bibtex

    Learning to View: Decision Transformers for Active Object Detection

    Wenhao Ding, Nathalie Majcherczyk, Mohit Deshpande, Xuewei Qi, Ding Zhao, Rajasimman Madhivanan, Arnie Sen
    IEEE International Conference on Robotics and Automation (ICRA) 2023, London

    arXiv / code / bibtex

    Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

    Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
    Conference on Neural Information Processing Systems (NeurIPS) 2022, New Orleans

    arXiv / code / bibtex

    SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles

    *Chejian Xu, *Wenhao Ding, Weijie Lyu, Zuxin Liu, Shuai Wang, Yihan He, Hanjiang Hu, Ding Zhao, Bo Li
    Conference on Neural Information Processing Systems (NeurIPS) 2022, New Orleans

    arXiv / code / bibtex

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

    Wenhao Ding, Chejian Xu, Mansur Arief, Haohong Lin, Bo Li, Ding Zhao
    To appear in IEEE Transactions on Intelligent Transportation Systems (T-ITS)

    arXiv / code / bibtex

    CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation

    Wenhao Ding, Haohong Lin, Bo Li, Ding Zhao
    Conference on Robot Learning (CoRL) 2022, New Zealand
    Abridged in ICML 2022 Workshop on Safe Learning for Autonomous Driving

    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, Xi'an

    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 (RA-L) with ICRA 2021, Xi'an

    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

    A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters

    Wenhao Ding, Wenshuo Wang, Ding Zhao
    International Conference on Robotics and Automation (ICRA) 2019, Montreal

    arXiv / code / supplementary / bibtex

    Preprints

    Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability

    *Mengdi Xu, *Zuxin Liu, *Peide Huang, Wenhao Ding, Zhepeng Cen, Bo Li, Ding Zhao
    Preprint arXiv:2209.08025

    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

    Academic Services

    Conference Reviewer: ICML, ICLR, NeurIPS (top reviewer), CVPR, ECCV, ICCV, ICRA, IROS, ICASSP, ICME

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

    Program Committee: ICRA 2022 SeasonDepth Challenge, NeurIPS 2022 ML4AD Workshop, NeurIPS 2022 TSRML Workshop, IJCAI 2022 AI4AD Workshop and Challenge

    Updated on March 21st, 2023

                 
    Wenhao Ding © 2018-2023