À propos
Dr. Jindong Wang is currently a Senior Researcher at Microsoft Research Asia. He obtained his Ph.D from Institute of Computing Technology, Chinese Academy of Sciences in 2019. He visited Qiang Yang’s group at Hong Kong University of Science and Technology in 2018. His research interest includes robust machine learning, transfer learning, semi-supervised learning, and federated learning. He has published over 50 papers with 6900 citations at leading conferences and journals such as ICLR, NeurIPS, TKDE, TASLP etc. He has 6 highly cited papers in Google Scholar metrics (opens in new tab). His paper “FedHealth” received the best application paper award at IJCAI FL workshop and it is the most cited paper among all federated learning for healthcare papers. He also received other awards including best paper award at ICCSE’18 and the prestigous excellent Ph.D thesis award (only 1 at ICT each year). In 2022 and 2023, he was selected as one of the AI 2000 Most Influential Scholars (opens in new tab) by AMiner between 2012-2022. He serves as the senior program committee member of IJCAI and AAAI, and PC members for top conferences like ICML, NeurIPS, ICLR, CVPR etc. He opensourced several projects to help build a better community, such as transferlearning, torchSSL, USB, personalizedFL, and robustlearn, which received over 12K stars on Github. He published a textbook Introduction to Transfer Learning (opens in new tab) to help starters quickly learn transfer learning. He gave tutorials at IJCAI’22 (opens in new tab), WSDM’23 (opens in new tab), and KDD’23 (opens in new tab).
Research interest: robust machine learning, out-of-distribution / domain generalization, transfer learning, semi-supervised learning, federated learning, and related applications such as activity recognition and computer vision. These days, I’m particularly interested in Large Language Models (LLMs) evaluation (opens in new tab) et robustness enhancement (opens in new tab).
For more information, please visit my personal website: http://www.jd92.wang (opens in new tab).