Donglin WANG, Ph.D.

School of Engineering

Machine Intelligence Lab (MiLAB)

CONTACT

Email: wangdonglin@westlake.edu.cn

Website:

Donglin WANG, Ph.D.

School of Engineering

Machine Intelligence Lab (MiLAB)

CONTACT

Email: wangdonglin@westlake.edu.cn

Website:

"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. P. Gao, Z. Liu, Z. Wu and D. Wang, “A Global Planning Algorithm for Robots Using Reinforcement Learning,” IEEE Robio, 2019.

2. Z. Chen, S. Gai and D. Wang, “Deep Tensor Factorization for Multi-Criteria Recommender Systems,” IEEE BigData, 2019.

3. Q. Tian*, J. Liu*, D. Wang and A. Tang, “Time Series Prediction with Interpretable Data Reconstruction,” ACM CIKM, 2019. (*Equal Contribution)

4. S. Huang, D. Wang, X. Wu and A. Tang, “DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting,” ACM CIKM, 2019.

5. S. Gai, F. Zhao, Y. Kang, D. Wang, and A. Tang, “Deep Transfer Collaborative Filtering for Recommender Systems,” PRICAI, 2019.

6. Y. Kang, F. Zhao, S. Gai, D. Wang, Z. Chen and Y. Luo, “Cross-Domain Deep Collaborative Filtering for Recommendation,” IEEE ICDMW, 2019.

7. S. Gai and D. Wang, “Sparse Model-Agnostic Meta-Learning Algorithm for Few-Shot Learning,” CCHI, 2019.

8. D. Wang, M. Fattouche, F. M. Ghannouchi and X. Zhan, "Quasi-Optimal Subcarrier Selection Dedicated for Localization With Multicarrier-Based Signals," in IEEE Systems Journal, vol. 13, no. 2, pp. 1157-1168, 2019. 

9. D. Wang, M. Fattouche and X. Zhan, "Pursuance of mm-Level Accuracy: Ranging and Positioning in mmWave Systems," in IEEE Systems Journal, vol. 13, no. 2, pp. 1169-1180, 2019.

10. F. Zhao, T. Huang and D. Wang, "A Probabilistic Approach for WiFi Fingerprint Localization in Severely Dynamic Indoor Environments," in IEEE Access, vol. 7, pp. 116348-116357, 2019.

11. D. Wang, T. Wang, F. Zhao and X. Zhang, "Improved Graph-Based Semi-Supervised Learning for Fingerprint-Based Indoor Localization," IEEE GLOBECOM, Dec. 2018.