Update 2024-8-12-acl24-tutorial.md

_posts/2024-8-12-acl24-tutorial.md

@@ -35,8 +35,8 @@ TBD
 ## Presenters: 
 
 |-------------|-------------|
-| ![Zhenyu](https://github.com/xuanwang91/xuanwang91.github.io/blob/master/images/img/Zhenyu_Bi.jpg?raw=True) | **Zhenyu Bi** is a Ph.D. student in the Computer Science Department at Virginia Tech. His research area lies in the field of natural language processing, emphasizing real-world applications of Large Language Models. He is mainly interested in information extraction with weak supervision, especially text mining and event extraction; as well as fact-checking and trustworthy NLP. He received an M.S. degree in Intelligent Information Systems from Carnegie Mellon University in 2023, a B.S. degree in Cognitive Science, and a B.S. Degree in Computer Science from the University of California, San Diego in 2021.  |
-| ![Minghao](https://github.com/xuanwang91/xuanwang91.github.io/blob/master/images/img/Minghao_Xu.jpg?raw=True) | **Minghao Xu** is a Ph.D. student at Mila - Quebec AI Institute, Canada. His research interests mainly lie in protein function understanding and protein design. He aims to understand diverse protein functions with joint guidance from protein sequences, structures, and biomedical text, especially boosted by large-scale multi-modal pre-training. He is also pursuing structure- and sequence-based protein design via generative AI, geometric deep learning and dry-wet experiment closed looping. He has given an Oral presentation at the main conference of ICML'23.  |
-| ![Jian](https://github.com/xuanwang91/xuanwang91.github.io/blob/master/images/img/Jian_Tang.jpg?raw=True) | **Jian Tang** is an Associate Professor at Mila - Quebec AI Institute, Canada. His long-term interests focus on understanding the language of life (DNA, RNAs, and Proteins) with generative AI and geometric deep learning, with applications in biomedicine and synthetic biology. His group has developed one of the first open-source machine learning frameworks on drug discovery, TorchDrug (for small molecules) and TorchProtein (for proteins), and developed the first diffusion models for 3D molecular structure generation, GeoDiff (among the 50 most cited AI paper in 2022). He has given a few tutorials at international AI and data mining conferences including KDD 2017, AAAI 2019, AAAI 2022.  |
-| ![Xuan](https://github.com/xuanwang91/xuanwang91.github.io/blob/master/images/img/Xuan2016.jpg?raw=True) | **Xuan Wang** is an Assistant Professor in the Computer Science Department at Virginia Tech. Her research focuses on natural language processing and text mining, emphasizing applications to science and healthcare domains. Her current projects include NLP and text mining with extremely weak supervision; text-augmented knowledge graph reasoning; fact-checking and trustworthy NLP, AI for science; and AI for healthcare. She received a Ph.D. degree in Computer Science, an M.S. degree in Statistics, and an M.S. degree in Biochemistry from the University of Illinois Urbana-Champaign in 2022, 2017, and 2015, respectively, and a B.S. degree in Biological Science from Tsinghua University in 2013. She has delivered tutorials in IEEE-BigData 2019, WWW 2022, and KDD 2022.  |
+| ![Zhenyu](https://github.com/xuanwang91/xuanwang91.github.io/blob/master/images/img/Zhenyu_Bi.jpg?raw=True) | **Zhenyu Bi** is a Ph.D. student in the Computer Science Department at Virginia Tech. His research area lies in the field of natural language processing, emphasizing real-world applications of Large Language Models. He is mainly interested in information extraction with weak supervision, especially text mining and event extraction; as well as fact-checking and trustworthy NLP. He received an M.S. degree in Intelligent Information Systems from Carnegie Mellon University in 2023, a B.S. degree in Cognitive Science, and a B.S. Degree in Computer Science from the University of California, San Diego in 2021. |
+| ![Minghao](https://github.com/xuanwang91/xuanwang91.github.io/blob/master/images/img/Minghao_Xu.jpg?raw=True) | **Minghao Xu** is a Ph.D. student at Mila - Quebec AI Institute, Canada. His research interests mainly lie in protein function understanding and protein design. He aims to understand diverse protein functions with joint guidance from protein sequences, structures, and biomedical text, especially boosted by large-scale multi-modal pre-training. He is also pursuing structure- and sequence-based protein design via generative AI, geometric deep learning and dry-wet experiment closed looping. He has given an Oral presentation at the main conference of ICML'23. |
+| ![Jian](https://github.com/xuanwang91/xuanwang91.github.io/blob/master/images/img/Jian_Tang.jpg?raw=True) | **Jian Tang** is an Associate Professor at Mila - Quebec AI Institute, Canada. His long-term interests focus on understanding the language of life (DNA, RNAs, and Proteins) with generative AI and geometric deep learning, with applications in biomedicine and synthetic biology. His group has developed one of the first open-source machine learning frameworks on drug discovery, TorchDrug (for small molecules) and TorchProtein (for proteins), and developed the first diffusion models for 3D molecular structure generation, GeoDiff (among the 50 most cited AI paper in 2022). He has given a few tutorials at international AI and data mining conferences including KDD 2017, AAAI 2019, AAAI 2022. |
+| ![Xuan](https://github.com/xuanwang91/xuanwang91.github.io/blob/master/images/img/Xuan2016.jpg?raw=True) | **Xuan Wang** is an Assistant Professor in the Computer Science Department at Virginia Tech. Her research focuses on natural language processing and text mining, emphasizing applications to science and healthcare domains. Her current projects include NLP and text mining with extremely weak supervision; text-augmented knowledge graph reasoning; fact-checking and trustworthy NLP, AI for science; and AI for healthcare. She received a Ph.D. degree in Computer Science, an M.S. degree in Statistics, and an M.S. degree in Biochemistry from the University of Illinois Urbana-Champaign in 2022, 2017, and 2015, respectively, and a B.S. degree in Biological Science from Tsinghua University in 2013. She has delivered tutorials in IEEE-BigData 2019, WWW 2022, and KDD 2022. |