add: talks

_bibliography/pubs.bib

@@ -9,7 +9,6 @@ @inproceedings{qin2023generalizable
   booktitle={The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
   year={2023},
   corr={true},
-  selected={true}
 }
 
 @inproceedings{zhang2023domain,
@@ -18,7 +17,12 @@ @inproceedings{zhang2023domain
   booktitle={The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)},
   year={2023},
 
-  arxiv={https://arxiv.org/abs/2208.08661}
+  arxiv={https://arxiv.org/abs/2208.08661},
+  selected={true},
+  code={https://github.com/yfzhang114/AdaNPC},
+  zhihu={https://zhuanlan.zhihu.com/p/631524930},
+  corr={true},
+  selected={true}
 }
 
 @inproceedings{yang2023glue,

_pages/about.md

@@ -21,7 +21,7 @@ jindongwang [at] outlook.com, jindong.wang [at] microsoft.com<br>
 
 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/).
 
-**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. See this [page](https://jd92.wang/research/) for more details. *Interested in [internship](https://zhuanlan.zhihu.com/p/102558267) or collaboration? Contact me.*
+**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) robustness. See this [page](https://jd92.wang/research/) for more details. *Interested in [internship](https://zhuanlan.zhihu.com/p/102558267) or collaboration? Contact me.*
 
 **Announcement:** I'm experimenting a new form of research collaboration. You can click [here](https://forms.office.com/r/32Fs6uAjT6) if you are interested!
 

_pages/others.md

@@ -9,15 +9,15 @@ 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 @ Carniege Mellon University.
+- 2023.05 -- present, [Hao Chen](https://scholar.google.com/citations?hl=en&user=tktqkhwAAAAJ&view_op=list_works&sortby=pubdate), PhD @ Carnegie Mellon University.
 - 2023.03 -- present, Kaijie Zhu, Master @ Institute of Automation, CAS.
 
 Alumni:
 
 - 2023.03 -- 2023.04, Lu Tan, Master @ Tsinghua University.
 - 2022.10 -- 2023.03, [Xixu Hu](https://xixuhu.github.io/), Ph.D @ City University of Hong Kong.
 - 2022.07 -- 2023.03, [Runkai Zheng](https://scholar.google.com/citations?user=52haRQ0AAAAJ&hl=en), Master @ Chinese University of Hong Kong (Shenzhen).
-- 2021.11 -- 2022.10, [Yidong Wang](https://qianlanwyd.github.io/), Master @ Tokyo Institute of Technology. Now: starts his Ph.D in PKU. 
+- 2021.11 -- 2022.10, [Yidong Wang](https://qianlanwyd.github.io/), Master @ Tokyo Institute of Technology. Now: Ph.D in PKU. [[MSRA official blog](https://www.msra.cn/zh-cn/news/outreach-articles/%e5%ae%9e%e4%b9%a0%e6%b4%be%ef%bd%9c%e7%8e%8b%e4%b8%80%e6%a0%8b%ef%bc%9a%e4%b8%bb%e5%8a%a8%e5%b0%b1%e4%bc%9a%e6%9c%89%e6%95%85%e4%ba%8b%ef%bc%81%e9%ab%98%e6%95%88%e7%a7%91%e7%a0%94%e7%a7%98%e8%af%80)]
   - Topics: semi-supervised learning, long-tail learning.
   - Publications during internship: NeurIPS'22, ACML'22, COLING'22
 - 2021.06 -- 2021.11, [Wang Lu](https://scholar.google.com.hk/citations?user=r0C8zaMAAAAJ&hl=zh-CN), Ph.D @ ICT, Chinese Academy of Sciences. Now: continue his Ph.D in ICT.

_pages/research.md

@@ -10,6 +10,7 @@ nav: true
 </center>
 
 The long-term research goal is to build robust models for modern AI, such as pre-trained models and large models. We create new theory, algorithms, applications, and open-sourced library to achieve our goal.
+These days, we are specifically interested in robustness in large language models (LLMs).
 
 Our research consists of the following topics with selected publications: [[View by year](https://jd92.wang/publications/)]
 

_pages/talks.md

@@ -12,7 +12,8 @@ nav: true
 
 #### Invited talks
 
-- Invited talk: **Robust machine learning for responsible AI**, at Hefei University of Technology. Mar. 2023.
+- Invited talk: **Safe, efficient, and generalizable transfer learning**, at NCIIP 2023. Changchun. May 2023.
+- Invited talk: **Robust machine learning for responsible AI**, at Hefei University of Technology. Mar. 2023. [[video at Bilibili](https://www.bilibili.com/video/BV1184y1M7V4/)]
 - Invited talk: **Building robust machine learning models**, at MLNLP community. Sep. 2022. [[Video & PDF](https://www.bilibili.com/video/BV1hP411V7SP/)]
 - Invited talk: **Transfer learning: low-resource, generalization, and safety**, at AI Time. Apr. 2022. [[PDF](../assets/files/l16_aitime.pdf)] [[Video](https://www.bilibili.com/video/BV1nY411E7Uc/)] [[Zhihu](https://zhuanlan.zhihu.com/p/498902783)]
 - Invited talk: **Recent advance in transfer learning**, at Jiqizhixin. Jun. 2021.
@@ -24,6 +25,7 @@ nav: true
 
 #### Invited Course
 
+- Invited course: **Transfer learning**, at Institute of Computing Technology, CAS. 2023.
 - Invited course: **Transfer learning**, at Tsinghua University. Dec. 2019. (THU's
                     advanced machine learning course for EE graduates) [[Class photo](http://jd92.wang/image/img_thu.png)]
 - Invited course: **Transfer learning**, at Leiphone, online. Nov. 4, 2017.