upd: info

_pages/about.md

@@ -19,7 +19,7 @@ Building 2, No. 5 Danling Street, Haidian District, Beijing, China<br>
 jindongwang [at] outlook.com, jindong.wang [at] microsoft.com<br>
 [Google scholar](https://scholar.google.com/citations?user=hBZ_tKsAAAAJ) | [DBLP](https://dblp.org/pid/19/2969-1.html) | [Github](https://github.com/jindongwang) || [Twitter](https://twitter.com/jd92wang) | [Zhihu](https://www.zhihu.com/people/jindongwang) | [Wechat](http://jd92.wang/assets/img/wechat_public_account.jpg) | [Bilibili](https://space.bilibili.com/477087194) || [CV](https://go.jd92.wang/cv) [CV (Chinese)](https://go.jd92.wang/cvchinese)
 
-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, and semi-supervised learning. He has published over 50 papers with 6800 citations at leading conferences and journals such as ICLR, NeurIPS, TKDE, TASLP etc. He has 6 highly cited papers in [Google Scholar metrics](https://www.aminer.cn/ai2000?domain_ids=5dc122672ebaa6faa962c2a4). In 2022, he was selected as one of the [2022 AI 2000 Most Influential Scholars](https://www.aminer.cn/ai2000?domain_ids=5dc122672ebaa6faa962c2a4) by AMiner between 2012-2021 (ranked 49/2000). He serves as the senior program committee member of IJCAI and AAAI, and PC members for other 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 11K stars on Github. He published a textbook [Introduction to Transfer Learning](http://jd92.wang/tlbook) in 2021 to help starters quickly learn transfer learning. He gave tutorials at [IJCAI'22](https://dgresearch.github.io/), [WSDM 2023](https://dgresearch.github.io/), and [KDD 2023](https://mltrust.github.io/).
+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](https://www.aminer.cn/ai2000?domain_ids=5dc122672ebaa6faa962c2a4). 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](https://www.aminer.cn/ai2000?domain_ids=5dc122672ebaa6faa962c2a4) 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](http://jd92.wang/tlbook) to help starters quickly learn transfer learning. He gave tutorials at [IJCAI'22](https://dgresearch.github.io/), [WSDM'23](https://dgresearch.github.io/), and [KDD'23](https://mltrust.github.io/).
 
 **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](https://llm-eval.github.io/) and [robustness enhancement](https://llm-enhance.github.io/). See this [page](https://jd92.wang/research/) for more details. *Interested in [internship](https://zhuanlan.zhihu.com/p/102558267) or collaboration? Contact me.*