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 / Resume / 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 interested in deep generative models for structured data, interpretable machine learning, and causal discovery. Specifically, I focus on understanding causations beyond correlations to develop robust and efficient robots.

I am an FPV drone flyer. Check my gallery.

2021/05 - I will start my summer intern in Bosch Center for Artificial Intelligence.

2021/02 - One paper accepted to IEEE Robotics and Automation Letter (with ICRA 2021).

2020/09 - One paper accepted to Neural Information Processing Systems (NeurIPS) 2020.

2020/06 - One paper accepted to International Conference on Intelligent Robots and Systems (IROS) 2020.

Semantically Controllable Scene Generation with Guidance of Explicit Knowledge

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

arXiv / code / bibtex

Deep Probabilistic Accelerated Evaluation: A Certifiable Rare-Event Simulation Methodology for Black-Box Autonomy

Mansur Arief*, Zhiyuan Huang*, Guru Kumar, Yuanlu Bai, Shengyi He, Wenhao Ding, Henry Lam, Ding Zhao
Artificial Intelligence and Statistics (AISTATS) 2021

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, Baimimng 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 and 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 and 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 and Ding Zhao
International Conference on Robotics and Automation (ICRA) 2019, Montreal.

arXiv / code / supplementary / bibtex

Hierarchical Reinforcement Learning Framework towards Multi-agent Navigation

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

arXiv / code / bibtex

Vehicle Pose and Shape Estimation through Multiple Monocular Vision

Wenhao Ding, Shuaijun Li, Guilin Zhang, Xiangyu Lei and Huihuan Qian
IEEE International Conference on Robotics and Biomimetics (ROBIO) 2018, Malaysia.

arXiv / code / video / bibtex

Conference Reviewer: CVPR 2022, ICLR 2022, NeurIPS 2021, ICCV 2021, ICRA 2020-2021, IROS 2020-2021, ICME 2020-2021

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

Wenhao Ding

丁文浩

News & Updates

Selected Publications ('*' indicates equal contribution)

Semantically Controllable Scene Generation with Guidance of Explicit Knowledge

Deep Probabilistic Accelerated Evaluation: A Certifiable Rare-Event Simulation Methodology for Black-Box Autonomy

Context-Aware Safe Reinforcement Learning for Non-Stationary Environments

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

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

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

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

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

Hierarchical Reinforcement Learning Framework towards Multi-agent Navigation

Vehicle Pose and Shape Estimation through Multiple Monocular Vision

Academic Services

Updated on October 16^th, 2021

Wenhao Ding © 2018-2021

Wenhao Ding

丁文浩

News & Updates

Selected Publications ('*' indicates equal contribution)

Semantically Controllable Scene Generation with Guidance of Explicit Knowledge

Deep Probabilistic Accelerated Evaluation: A Certifiable Rare-Event Simulation Methodology for Black-Box Autonomy

Context-Aware Safe Reinforcement Learning for Non-Stationary Environments

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

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

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

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

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

Hierarchical Reinforcement Learning Framework towards Multi-agent Navigation

Vehicle Pose and Shape Estimation through Multiple Monocular Vision

Academic Services

Updated on October 16th, 2021

Wenhao Ding © 2018-2021

Updated on October 16^th, 2021