upd: personal info

jindongwang · Sep 17, 2023 · 6f64656 · 6f64656
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diff --git a/_pages/about.md b/_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)
 
-I'm currently a Senior Researcher at [Microsoft Research Asia (MSRA)](http://www.msra.cn/), in a group managed by [Xing Xie](https://www.microsoft.com/en-us/research/people/xingx/). Before joining MSRA, I obtained my Ph.D. from Institute of Computing Technology, Chinese Academy of Sciences in June, 2019. My doctoral thesis was awarded the excellent Ph.D. thesis of Chinese Academy of Sciences. In 2018/04--2018/08, I was a visitor of Prof. [Qiang Yang](https://cse.hkust.edu.hk/~qyang/)'s group at Hong Kong University of Science and Technology (HKUST). My work on transfer learning won the best paper awards in ICCSE 2018 and FTL-IJCAI 2019. In 2021, I published the textbook [Introduction to Transfer Learning](http://jd92.wang/tlbook), a hands-on introduction to transfer learning. In 2022, I 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). Four of my first-author papers are ranked by Google Scholar as [highly-cited papers](https://zhuanlan.zhihu.com/p/421192644). I gave tutorials at [IJCAI'22](https://dgresearch.github.io/), WSDM 2023, and KDD 2023.
+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/).
 
 **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.*
 

diff --git a/_pages/others.md b/_pages/others.md
@@ -10,10 +10,11 @@ nav: true
 Current interns:
 
 - 2023.05 -- present, [Hao Chen](https://scholar.google.com/citations?hl=en&user=tktqkhwAAAAJ&view_op=list_works&sortby=pubdate), PhD @ Carnegie Mellon University.
-- 2023.06 -- present, Yachuan Liu, PhD @ University of Michigan, Ann Arbor.
+- 2023.09 -- present, Cheng Li, master @ Institute of Software, CAS.
 
 Alumni:
 
+- 2023.06 -- 2023.09, Yachuan Liu, PhD @ University of Michigan, Ann Arbor.
 - 2023.03 -- 2023.06, Kaijie Zhu, Master @ Institute of Automation, CAS.
   - Topics: adversarial machine learning and large language language models.
   - Outcomes during internship: ICCV'23, [PromptBench](https://github.com/microsoft/promptbench), [Project SearchAnything](https://github.com/Immortalise/SearchAnything).