FACULTY

Faculty

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Donglin Wang, Ph.D.

Donglin Wang, Ph.D.

Donglin Wang, Ph.D.

School of Engineering

Artificial Intelligence and Data Science (AI)

School of Engineering

联系

网站: https://milab.westlake.edu.cn/

"Devote all myself into scientific research. Contribute my efforts to rising and soaring of Westlake Institute of Advanced Studies (WIAS). Wish WIAS to become a world-class research institute standing by the Westlake."

Biography

Donglin Wang is the principal investigator of the National Science and Technology Innovation 2030 Major Project (Chief Scientist), and the Chair of Artificial Intelligence and Data Science Program, School of Engineering, Westlake University. He received the B.E. and M.S. degrees in the school of electronics and information engineering from Xi'an Jiaotong University, China, in 2003 and 2006, respectively, and the Ph.D. degree in the department of electrical and computer engineering from the University of Calgary, Canada, in 2010. After that, he acted as a postdoc research fellow in the iRadio lab, Canada. From late 2011 to Aug. 2017, he was an assistant/associate professor in the department of electrical and computer engineering at New York Institute of Technology. He joined Westlake University in 2017 and is now a tenured associate professor, and the director of the Machine Intelligence Laboratory (MiLAB). Starting in Sep. 2023, he has been the Chair of Artificial Intelligence and Data Science Program.

History

2017

Associate Professor, School of Engineering, Westlake University  

2011

Assistant/associate professor, New York Institute of Technology, USA 

Postdoctoral research, iRadio lab, University of Calgary, Canada

2010

Ph.D. degree,  University of Calgary, Canada

2006

Master's degree, Xi'an Jiaotong University, China

2003

Bachelor's degree, Xi'an Jiaotong University, China

Research

Machine Intelligence Laboratory (MiLAB) is mainly engaged in the field of robot learning, in-depth research on how to use machine learning theory to improve robot behavioral intelligence. Focus on the following research directions:

1. Deep Reinforcement Learning;

2. Embodied Learning and Intelligence.

The research on Deep reinforcement learning and embodied intelligence is aimed to improve the general behavioral intelligence of robots such as behavioral flexibility, behavioral autonomous learning ability and rapid adaptability. More than 100 international academic papers have been published, where approximately 70 papers have been published in the past five years, including AI top-tier conferences and international journals such as NeurIPS, ICML, ICLR, CVPR, AAAI. They have also applied for more than 10 patents and developed a distinctive footed robot. For more details, please visit the laboratory's homepage: https://milab.westlake.edu.cn/.

Representative Publications

[1] J. Liu, L. He, Y. Kang, Z. Zhuang, D. Wang*, H. Xu, "CEIL: Generalized Contextual Imitation Learning", Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS), 2023.

[2] J. Liu, L. Zu,L. He, D. Wang*, "CLUE: Calibrated Latent Guidance for Offline Reinforcement Learning", Conference on Robot Learning (CoRL), 2023.

[3] Y. Kang, D. Shi, J. Liu, L. He, D. Wang*. “Beyond Reward: Offline Preference-guided Policy Optimization,” In Proceedings of the Fortieth International Conference on Machine Learning (ICML), 2023.

[4] S. Huang, B. Gong, Y. Pan, J. Jiang, Y. Lv, Y. Li, D.Wang*. "VoP: Text-Video Co-operative Prompt Tuning for Cross-Modal Retrieval," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.

[5] Z. Zhuang, K. Lei, J. Liu, D. Wang*, Y. Guo. "Behavior Proximal Policy Optimization," In Proceedings of The International Conference on Learning Representations (ICLR), 2023.

[6] J. Liu, H. Zhang, D. Wang*, "DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning," International Conference on Learning Representations (ICLR), 2022.

[7] M. Zhang, S. Huang, W. Li, D. Wang*, "Tree Structure-Aware Few-Shot Image Classification via Hierarchical Aggregation," European Conference on Computer Vision (ECCV), 2022.

[8] T. Xiao, Z. Chen, D. Wang*, S. Wang, "Learning How to Propagate Messages in Graph Neural Networks", ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2021.

[9] J. Liu, H. Shen, D. Wang*, Y. Kang, Q. Tian, "Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning," Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS), 2021.

[10] Z. Chen, J. Ge, H. Zhan, S. Huang, D. Wang*, "Pareto Self-Supervised Training for Few-Shot Learning," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

*A list of published papers can be found on laboratory's homepage: https://milab.westlake.edu.cn/.

Main Projects:

1. National Science and Technology Innovation 2030- Major Project (tens of millions of projects), "Deep Reinforcement Learning Methods for Brain-like Chips", 2022-2027, PI.

2. National Natural Science Foundation of China, "Theory and Application of Reinforcement Learning in the Behavioral Intelligence of Quadruped Robots", 2022-2025, PI.

3. Key Project of Zhejiang Natural Science Foundation, "A New Height in the Field of Positioning -- Theoretical Research on Millimeter Level Ultra-High Precision Wireless Positioning", 2019-2022, PI.

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