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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.
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Research Highlights
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:
News & Updates
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.
Selected Publications
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
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
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
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
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
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
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
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
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
Preprints
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
Academic Services
Conference Reviewer: ICML, ICLR, NeurIPS, AISTATS, CVPR, ECCV, ICCV, ICRA, IROS, ICASSP, ITSC, ICME
Journal Reviewer: TMLR, IEEE RA-L, IEEE TNNLS, IEEE T-ITS, IEEE T-IV, IEEE Access, IEEE TII, IEEE MM
Organizer: CVPR 2023 Secure and Safe Autonomous Driving Workshop and Challenge, ICRA 2022 SeasonDepth Challenge