Donglin WANG, Ph.D.

School of Engineering

Machine Intelligence Lab (MiLAB)

CONTACT

Email: wangdonglin@westlake.edu.cn

Website: https://milab.westlake.edu.cn/

Donglin WANG, Ph.D.

School of Engineering

Machine Intelligence Lab (MiLAB)

CONTACT

Email: wangdonglin@westlake.edu.cn

Website: 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 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 is now an associate professor and director of Machine Intelligent Lab (MiLAB) in School of Engineering at Westlake University.


Research

Our lab is focusing on robot learning, including machine learning algorithms, intelligent robot and data mining.

1. Machine learning algorithms: reinforcement learning, Meta-learning, one-shot learning, and curriculum learning;

2. Robot Intelligence: learning based flexibility, fast adaptation, behavior development, lifelong memory and human-like navigation;

3. Data mining: Time-series prediction, recommendation and applications.


Representative Publications

[1]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-21), 2021.

[2]F. Zhao, D. Wang* and X. Xiang, "Multi-Initialization Graph Meta-Learning for Node Classification," ACM International Conference on Multimedia Retrieval (ICMR-21), 2021.

[3] Z. Zhuang, X. Xiang, S. Huang, D. Wang*, "HINFShot: A Challenge Dataset for Few-Shot Node Classification in Heterogeneous Information Network," ACM International Conference on Multimedia Retrieval (ICMR-21), 2021.

[4] 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-21), 2021.

[5] S. Huang, M. Zhang, Y. Kang, and D. Wang*, “Attributes-Guided and Pure-Visual Attention Alignment for Few-Shot Recognition,” In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021.

[6] T. Xiao and D. Wang*, “A General Offline Learning Framework for Interactive Recommendation,” In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021.

[7] Z. Chen, Z. Xu, and D. Wang*, “Deep Transfer Tensor Decomposition with Orthogonal Constraint for Recommender Systems,” In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), 2021.

[8] Q. Tian, G. Wang, J. Liu, D. Wang* and Y. Kang, “Independent Skill Transfer for Deep Reinforcement Learning,” In Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI), IJCAI-PRICAI 2020, Yokohama Japan, 2020.

[9] M. Zhang, D. Wang* and S. Gai, “Knowledge Distillation for Model-agnostic Meta-learning,” In Proceedings of the 24th European Conference on Artificial Intelligence (ECAI), Santiago de Compostela, Spain, 2020.

[10] T. Huang*, Z. Zhuang*, S. Zhang, D. Wang*, “Homogenization with Semantics Preservation of Heterogeneous Information Network,” In Proceedings of the 29th Conference on Information and Knowledge Management (CIKM), Virtual Meeting, 2020. (*Equal Contribution)

[11] Q. Tian*, J. Liu*, D. Wang* and A. Tang, “Time Series Prediction with Interpretable Data Reconstruction,” In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), Beijing, 2019. (*Equal Contribution)

[12] S. Huang, D. Wang*, X. Wu and A. Tang, “DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting,” In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM), Beijing, 2019.