王东林博士
Donglin Wang, Ph.D.
工学院

机器智能实验室
联系
邮箱: wangdonglin@westlake.edu.cn
网站: https://milab.westlake.edu.cn/





王东林博士
Donglin Wang, Ph.D.
工学院

机器智能实验室
联系
邮箱: wangdonglin@westlake.edu.cn
网站: https://milab.westlake.edu.cn/



“简简单单做人，全心全意科研。希望能为西湖大学的崛起和腾飞贡献自己的一份力量。期待西湖大学未来能够成为矗立在西子湖畔的世界一流研究中心。”


个人简介

王东林，1999年进入西安交通大学信息工程系，2003、2006年分别获学士和硕士学位；2010年获加拿大卡尔加里大学电子与计算机工程系博士学位。随后在加拿大iRadio Lab从事博士后研究工作，2011年底加入美国纽约理工学院电子与计算机工程系担任助理教授，2016年晋升副教授。2017年9月全职加盟西湖大学，担任工学院特聘研究员，副教授，机器智能实验室PI。

学术成果

机器智能实验室（MiLAB）主要从事机器人学习（Robot Learning）领域的研究，包括机器学习算法、机器人智能化和数据挖掘算法等。
　　1. 深度强化学习理论和机器人应用
　　2. 元学习理论、小样本学习理论和机器人应用
　　3. 机器人智能化：基于机器学习算法的机器人高度灵活性、快速适应性、持续记忆和VLN导航

近期代表论文


[1] 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.
[2] 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.
[3] 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.
[4] 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)
[5] 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.
[6] 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.
[7] J. Liu, Q. Tian, D. Wang and X. Yang “RDRM: Residual Dual-Recurrent Model for Multivariate Long-Term Time-Series Prediction,” In Proceedings of the International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020.
[8] Y. Kang*, S. Gai*, F. Zhao, D. Wang and A. Tang, “Deep Transfer Collaborative Filtering with Geometric Structure Preservation for Cross-Domain Recommendation,” In Proceedings of the International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020. (*Equal Contribution)
[9] 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)
[10] 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.
[11] Z. Chen, S. Gai and D. Wang, “Deep Tensor Factorization for Multi-Criteria Recommender Systems,” In Proceedings of the IEEE International Conference on Big Data (BigData), Los Angeles, LA, 2019.
[12] S. Gai, F. Zhao, Y. Kang, D. Wang, and A. Tang, “Deep Transfer Collaborative Filtering for Recommender Systems,” In Proceedings of the Pacific Rim International Conferences on Artificial Intelligence (PRICAI), Yanuca Island, Cuvu, Fiji, 2019.
[13] 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.
[14] 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.
[15] D. Wang,T. Wang, F. Zhao and X. Zhang, "Improved Graph-Based Semi-Supervised Learning for Fingerprint-Based Indoor Localization,"In Proceedings of the IEEE Global Communications Conference (GLOBECOM), Abu Dhabi, UAE, Dec. 2018.



联系方式

电子邮箱：wangdonglin@westlake.edu.cn
实验室招聘及专业要求
1. 招聘研究员、副研究员、助理研究员：要求具有深度学习或强化学习研究背景，协助指导博士生；
2. 招聘博士后：计算机、通信、控制、人工智能等信息类专业博士均可，要求发表过至少一篇高质量论文;
3. 招聘科研助理：计算机、通信、控制等相关专业背景，要求具有深度学习经验，程序能力强；
4. 招聘实习生或访问学生：了解深度学习，要求程序能力或数学能力好。
请有意向者发简历到MiLAB实验室邮箱：mi_lab@westlake.edu.cn。合适的申请人我们会及时联系；由于收到申请邮件较多，不满足岗位需求的将不予回复，请见谅。