Jindong Wang (王晋东)


Machine Learning Group, Microsoft Research Asia (MSRA)

jindong.wang (at) microsoft.com, jindongwang (at) outlook.com

Google Scholar | Zhihu Github Weibo | [CV]

Internship: If you are interested in working with me as an intern, please feel free to contact me.

Short Bio
I'm currently a researcher at machine learning group, Microsoft Research Asia (MSRA), where I directly report to Prof. Tie-Yan Liu. Before joining MSRA, I obtained by Ph.D degree from Institute of Computing Technology, Chinese Academy of Sciences in June, 2019. My Ph.D thesis is mainly about transfer learning algorithms. At MSRA, my research interest will be transfer learning based few-shot learning, meta learning, adaptive learning, and related applications.
My personal page on Microsoft Research

[Research] [Education] [Publications] [Talks] [Projects] [Awards & Service] [Skill] [Person]

Research Experience

Technical reports

  1. 迁移学习简明手册 (First manual in transfer learning).
    Jindong Wang.
    Transfer Learning Tutorial 2018
    [Link] [PDF] [Github]

Conference papers

  1. Transfer Learning with Dynamic Adversarial Adaptation Network.
    Chaohui Yu*, Jindong Wang*, Yiqiang Chen, Meiyu Huang. (* denotes equal contribution)
    IEEE International Conference on Data Mining (ICDM) 2019 (Full paper, 9.08% acceptance rate)
  2. FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare.
    Yiqiang Chen, Jindong Wang, Chaohui Yu.
    IJCAI-19 Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (IJCAI (FML)) 2019 (Best Application Paper)
  3. DrowsyDet: A Mobile Application for Real-Time Driver Drowsiness Detection.
    Chaohui Yu, Xin Qin, Yiqiang Chen, Jindong Wang, Chenchen Fan.
    IEEE International Conference on Ubiquitous Intelligence and Computing (UIC) 2019
  4. Easy Transfer Learning By Exploiting Intra-domain Structures.
    Jindong Wang, Yiqiang Chen, Han Yu, Meiyu Huang, Qiang Yang.
    IEEE International Conference on Multimedia & Expo (ICME) 2019
    [PDF] [Code]
  5. Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Prunning.
    Chaohui Yu, Jindong Wang, Yiqiang Chen, Zijing Wu.
    Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2019 Workshop
    International Joint Conference on Neural Networks (IJCNN) 2019
    [PDF] [Code]
  6. Visual Domain Adaptation with Manifold Embedded Distribution Alignment.
    Jindong Wang, Wenjie Feng, Yiqiang Chen, Han Yu, Meiyu Huang, Philip S. Yu.
    ACM International Conference on Multimedia (ACMMM) 2018 (Oral, Top 10 papers)
    [PDF] [Code] [Supplementary] [Poster]
  7. Deep Transfer Learning for Cross-domain Activity Recognition.
    Jindong Wang, Vincent W. Zheng, Yiqiang Chen, Meiyu Huang.
    The 3rd International Conference on Crowd Science and Engineering (ICCSE) 2018 (Best Paper)
    [PDF] [Link]
  8. Stratified Transfer Learning for Cross-domain Activity Recognition.
    Jindong Wang, Yiqiang Chen, Lisha Hu, Xiaohui Peng, Philip S. Yu.
    IEEE International Conference on Pervasive Computing and Communications (PerCom) 2018
    [PDF] [Code]
  9. Balanced Distribution Adaptation for Transfer Learning.
    Jindong Wang, Yiqiang Chen, Shuji Hao, Wenjie Feng, Zhiqi Shen.
    The IEEE International Conference on Data Mining (ICDM) 2017
    [Link] [PDF] [Code]
  10. Weak Multipath Effect Identification for Indoor Distance Estimation.
    Xiaohai Li, Yiqiang Chen, Zhongdong Wu, Xiaohui Peng, Jindong Wang, Lisha Hu, Diancun Yu.
    The 14th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC) 2017
  11. OCEAN: A New Opportunistic Computing Model for Wearable Activity Recognition.
    Yiqiang Chen, Yang Gu, Xinlong Jiang, Jindong Wang.
    The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) 2016
    [PDF] [Link]
  12. Less Annotation on Personalized Activity Recognition Using Context Data.
    Lisha Hu, Yiqiang Chen, Shuangquan Wang, Jindong Wang, Jianfei Shen, Xinlong Jiang, Zhiqi Shen.
    The 13th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC) 2016
    [PDF] [Link]

Journal articles

  1. Transfer Learning with Dynamic Distribution Adaptation.
    Jindong Wang, Yiqiang Chen, Wenjie Feng, Han Yu, Meiyu Huang, Qiang Yang.
    ACM Transactions on Intelligent Systems and Technology (TIST) (Accepted)
  2. Transfer Channel Prunning for Deep Unsupervised Domain Adaptation.
    Chaohui Yu, Jindong Wang, Yiqiang Chen, Xin Qin.
    International Journal of Machine Learning and Cybernetics (IJMLC) (Accepted)
    [Link] [PDF] [Code]
  3. Multi-representation Adaptation Network for Cross-domain Image Classification.
    Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Jingwu Chen, Zhiping Shi, Wenjuan Wu, Qing He.
    Neural Networks Volume 119, November 2019, Pages 214-221
    [Link] [Code]
  4. Cross-position Activity Recognition with Stratified Transfer Learning.
    Yiqiang Chen, Jindong Wang, Meiyu Huang, Han Yu.
    Pervasive and Mobile Computing Volume 57, July 2019, Pages 1-13
    [Link] [PDF]
  5. Deep Learning for Sensor-based Activity Recognition: A Survey.
    Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, Lisha Hu.
    Pattern Recognition Letters Volume 119, 1 March 2019, Pages 3-11
    [Link] [PDF]
  6. OKRELM: Online Kernelized and Regularized Extreme Learning Machine for Wearable-based Activity Recognition.
    Lisha Hu, Yiqiang Chen, Jindong Wang, Chunyu Hu, Xinlong Jiang.
    International Journal of Machine Learning and Cybernetics (IJMLC) 2017
    [Link] [PDF]

Book chapters

  1. 保研一本通. 2014 [JD Link] [PDF]
  1. Transfer learning: challenges and methods. Shandong University. 2018.10
  2. 迁移学习问题与方法. 深圳大学 & 哈尔滨工业大学(深圳) & 哈尔滨理工大学. 2018.06-07 [PDF] [News]
  3. 迁移学习中的领域自适应方法. 极视角学术分享 & 全球人工智能学术分享. 2017.12.14-21 [PDF] [Video]
  4. 迁移学习的发展与现状. AI研习社公开课-雷锋网. 2017.11.04 [PDF] [News] [Course Link]
  5. On Machine Learning (Chinese). AI Salon at Shanghai Jiao Tong University. 2017.09.16 [PDF] [Photo]
  6. Distant Domain Transfer learning. Lab presentation. 2017.05.19 [PDF]
  7. Transfer learning (Chinese version). Regular. 2017.04 [PDF]
  8. Feature engineering in machine learning. Zhihu Live. 2017.03.19 [PDF] [Visit Live]
  9. Machine learning starter. Zhihu Live. 2016.12.22 [PDF] [Visit Live]
  10. A few tips for deep learning starter. Lab presentation. 2016.11.10 [PDF]
  11. Transfer Learning based Activity Recognition via Domain Adaptation. Machine Learning class presentation. 2016.06.27 [PDF]
  12. Introduction to Transfer Learning. Lab presentation. 2016.06.03 [PDF]
  13. Eigenface vs fisherfaces: recognition using class specific linear projection. Machine learning class presentation. 2016.05.26 [PDF]
  14. Community similarity networks. Lab presentation. 2015.09.28 [PDF]
  1. See my Github rankings all over the world: [Link]
  2. Leading the transfer learning tutorial (迁移学习简明手册) on Github: Tutorial
  3. Leading a pupular transfer learning resource projects on Github: Transfer Learning
  4. I'm also leading other popular research projects: Machine Learning, Activity Recognition
  5. Numberconverter: a simple wox plugin to convert numbers into certain systems. 2015
  6. My BMI & 2048 : a simple BMI app, while 2048 is a modified 2048 game about study. Available at Apple's App Store. 2015
  7. I started a software studio Pivot Studio with a group of talented people, and we made many applications. 2010-2014
Awards and professional activities
Awards and Honors
Academic service
Reviewer | PC Member