Wenhao Ding

Wenhao Ding (丁文浩)

Email: wenhaod AT andrew DOT cmu DOT edu

I am a final-year Ph.D. in Safe AI Lab at Carnegie Mellon University, co-advised by Prof. Ding Zhao and Prof. Bo Li. I received my Master's degree from Machine Learning Department at CMU and Bachelor degree from Electronic Engineering at Tsinghua University.

I am looking for postdoc and industry positions starting in 2024.

Research Intern
A.S. Intern
Research Intern
Ph.D. Candidate
2023.6 - Now
2022.5 - 2022.9
2021.5 - 2021.9
2019.8 - Now
2014.8 - 2018.7

My research lies in closing the loop of agent and environment from the data perspective. I believe that building safe, robust, and generalizable autonomy relies on both powerful algorithms and suitable environments for training and validating the algorithms. To achieve this goal, I work on the follwing directions:

  • Increase data coverage. Generate critical scenarios for scaling up training and validation.
  • Increase data efficiency. Discover underlying structure for learning compositional representation.
  • 2023/09 - One paper combining causality and robustness is accepted to NeurIPS 2023.

    2023/06 - I will start my internship in NVIDIA Research Autonomous Vehicle Research Group.

    2023/04 - One paper about reward-conditioned offline RL accepted to ICML 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!

    2022/08 - My research proposal wins 2022 Qualcomm Innovation Fellowship, North America.

    Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation

    *Wenhao Ding, *Laixi Shi, Yuejie Chi, Ding Zhao
    Conference on Neural Information Processing Systems (NeurIPS) 2023, New Orleans

    website / arXiv / code / bibtex

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

    *Wenhao Ding, *Tong Che, Ding Zhao, Marco Pavone
    International Conference on Machine Learning (ICML) 2023, Hawaii

    arXiv / code / bibtex

    What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery

    Peide Huang, *Xilun Zhang, *Ziang Cao, *Shiqi Liu, Mengdi Xu, Wenhao Ding, Jonathan Francis, Bingqing Chen, Ding Zhao
    Conference on Robot Learning (CoRL) 2023, Atlanta

    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

    website / 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
    IEEE Transactions on Intelligent Transportation Systems (T-ITS), March, 2023

    arXiv / Xplore / 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

    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

    Safety-aware Causal Representation for Trustworthy Reinforcement Learning in Autonomous Driving

    Haohong Lin, Wenhao Ding, Zuxin Liu, Yaru Niu, Jiacheng Zhu, Yuming Niu, Ding Zhao
    Preprint arXiv:2311.10747

    arXiv / code / bibtex

    Your Room is not Private: Gradient Inversion Attack for Deep Q-Learning

    Miao Li, Wenhao Ding, Ding Zhao
    Preprint arXiv:2306.09273

    arXiv / code / bibtex

    Semantically Adversarial Driving Scenario Generation with Explicit Knowledge Integration

    Wenhao Ding, Haohong Lin, Bo Li, Kim Ji Eun, Ding Zhao
    Workshop on Environment Generation for Generalizable Robots (EGG) at RSS 2023
    Workshop on Knowledge and Logical Reasoning in the Era of Data-driven Learning at ICML 2023

    arXiv / code / bibtex

    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



    Organizer: CVPR 2023 Secure and Safe Autonomous Driving Workshop and Challenge, ICRA 2022 SeasonDepth Challenge

    © Copyright 2023 Wenhao Ding. Last Updated: November 23th, 2023