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, advised by Prof. Ding Zhao. I received my Bachelor degree from Electronic Engineering at Tsinghua University. I work closely with Prof. Bo Li and Prof. Marco Pavone. I was honored with the Qualcomm Innovation Fellowship in 2022.

             2024 -
I will join the Autonomous Vehicle Group in Nvidia Research as a Research Scientist. I work on the closed-loop simulation and safety for autonomous systems.
2019 - 2024
My Ph.D. research focused on safety-critical scenarios generation. I was interested in reinforcement learning, deep generative model, and causal discovery.
2023 - 2023
I was a Research Intern in the Autonomous Vehicle Group led by Prof. Marco Pavone. I collaborated with Chaowei Xiao and Yulong Cao on a scenario generation project.
2022 - 2022
I was an Applied Scientist Intern of the Astro team at Lab126, where I collaborated with Nathalie Majcherczyk and Mohit Deshpande on the active perception project.
2021 - 2021
I was a Machine Learning Research Intern at Bosch Center for Artificial Intelligence, where I worked on traffic flow analysis and clustering.
2014 - 2018
I received my Bachelor degree from the Department of Electronic Engineering at Tsinghua University. I was a member of the Spark Program and a former leader of Skyworks.

I work on deep generative models, reinforcement learning, and causal discovery. My research advances robot learning in open-ended world from data perspective:

  • Data Acquisition: generating critical and diverse scenarios for expanding data coverage.
  • Data Representation: discovering the underlying structure of data for generalization and robustness.
  • Data Consumption: designing in-context and fine-tuning strategy for continual model learning.
  • 2024/03 - One paper using causal-aware representation for driving is accepted to RA-L.

    2024/02 - One paper about privacy in robotics is accepted to ICRA 2024.

    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!

    CaDRE: Controllable and Diverse Generation of Safety-Critical Driving Scenarios using Real-World Trajectories

    Peide Huang, Wenhao Ding, Jonathan Francis, Bingqing Chen, Ding Zhao,
    Preprint arXiv:2403.13208

    arXiv / code / bibtex

    RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios

    Wenhao Ding*, Yulong Cao*, Ding Zhao, Chaowei Xiao, Marco Pavone
    Preprint arXiv:2312.13303

    website / 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

    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
    IEEE Robotics and Automation Letters (RA-L) 2024

    arXiv / code / bibtex

    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

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

    Miao Li, Wenhao Ding, Ding Zhao
    International Conference on Robotics and Automation (ICRA) 2024, Japan

    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

    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

    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

    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

    Conference Reviewer: ICML, ICLR, NeurIPS, AISTATS, CVPR, ECCV, ICCV, CoRL, ICRA, IROS, IJCAI, ICASSP, ITSC, ICME

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

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


    © Copyright 2024 Wenhao Ding

    Last Updated: April 28th, 2024