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Monday Morning 10am-12:30pm, December 04, 2023 (All times in Beijing Time)
The workshop will be hybrid.
Please attend in-person in Room 6 or virtually on Zoom (meeting ID 91649466943 with PW 202312).
10:00 - 10:10
Introduction & Opening Remarks
Introduction & Opening Remarks
By Dr. Carl Yang
10:10 - 10:30
Keynote Talk 1: Modality Agnostic Federated Learning
Keynote Talk 1: Modality Agnostic Federated Learning
By Dr. Aidong Zhang
10:30 - 10:50
Keynote Talk 2: Trustworthy graph learning in the era of large language models
Keynote Talk 2: Trustworthy graph learning in the era of large language models
By Dr. Bo Li
10:50 - 11:10
11:10 - 11:30
Keynote Talk 4: Federated Algorithms: On-device Learning and Beyond
Keynote Talk 4: Federated Algorithms: On-device Learning and Beyond
By Dr. Zheng Xu
11:30-12:00
Data Challenge Presentations
Data Challenge Presentations
Moderator: Yuhang Yao
12:00 - 12:30
Oral/Spotlight Paper Presentations
Oral/Spotlight Paper Presentations
Moderator: Ke Zhang
The Keynote Speakers
The Keynote Speakers
Dr. Aidong Zhang develops machine learning and data science approaches to modeling and analysis of structured and unstructured data with a variety of applications, especially biological and biomedical applications. Dr. Zhang is Thomas M. Linville Professor of Computer Science, with a joint appointment in the Department of Biomedical Engineering and School of Data Science at University of Virginia. Her research interests focus on machine learning, data mining, bioinformatics and health informatics.
Dr. Aidong Zhang develops machine learning and data science approaches to modeling and analysis of structured and unstructured data with a variety of applications, especially biological and biomedical applications. Dr. Zhang is Thomas M. Linville Professor of Computer Science, with a joint appointment in the Department of Biomedical Engineering and School of Data Science at University of Virginia. Her research interests focus on machine learning, data mining, bioinformatics and health informatics.
Dr. Bo Li is Neubauer Associate Professor in the Department of Computer Science at the University of Chicago. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, NSF CAREER Award, AI's 10 to Watch, MIT Technology Review TR-35 Award, Dean's Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star Award, Symantec Research Labs Fellowship, Rising Stars in EECS, Research Awards from Tech companies such as Amazon, Meta, Google, Intel, MSR, eBay, and IBM, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, which is at the intersection of machine learning, security, privacy, and game theory. She has designed several scalable frameworks for robust learning and privacy-preserving data publishing systems. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and New York Times.
Dr. Bo Li is Neubauer Associate Professor in the Department of Computer Science at the University of Chicago. She is the recipient of the IJCAI Computers and Thought Award, Alfred P. Sloan Research Fellowship, NSF CAREER Award, AI's 10 to Watch, MIT Technology Review TR-35 Award, Dean's Award for Excellence in Research, C.W. Gear Outstanding Junior Faculty Award, Intel Rising Star Award, Symantec Research Labs Fellowship, Rising Stars in EECS, Research Awards from Tech companies such as Amazon, Meta, Google, Intel, MSR, eBay, and IBM, and best paper awards at several top machine learning and security conferences. Her research focuses on both theoretical and practical aspects of trustworthy machine learning, which is at the intersection of machine learning, security, privacy, and game theory. She has designed several scalable frameworks for robust learning and privacy-preserving data publishing systems. Her work has been featured by major publications and media outlets such as Nature, Wired, Fortune, and New York Times.
Dr. Jiayu Zhou is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University. He received his Ph.D. degree in computer science from Arizona State University in 2014. He has a broad research interest in large-scale machine learning and data mining, and biomedical informatics. He served as a technical program committee member of premier conferences such as NIPS, ICML, and SIGKDD. Jiayu’s research is supported by the National Science Foundation, the National Institutes of Health, and the Office of Naval Research. He is a recipient of the National Science Foundation CAREER Award (2018). His papers received the Best Student Paper Award at the 2014 IEEE International Conference on Data Mining (ICDM), the Best Student Paper Award at the 2016 International Symposium on Biomedical Imaging (ISBI), the Best Paper Award at the 2016 IEEE International Conference on Big Data (BigData), and Best Paper Award in Health Track, the 2022 SIGKDD Conference on Knowledge Discovery and Data Mining.
Dr. Jiayu Zhou is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University. He received his Ph.D. degree in computer science from Arizona State University in 2014. He has a broad research interest in large-scale machine learning and data mining, and biomedical informatics. He served as a technical program committee member of premier conferences such as NIPS, ICML, and SIGKDD. Jiayu’s research is supported by the National Science Foundation, the National Institutes of Health, and the Office of Naval Research. He is a recipient of the National Science Foundation CAREER Award (2018). His papers received the Best Student Paper Award at the 2014 IEEE International Conference on Data Mining (ICDM), the Best Student Paper Award at the 2016 International Symposium on Biomedical Imaging (ISBI), the Best Paper Award at the 2016 IEEE International Conference on Big Data (BigData), and Best Paper Award in Health Track, the 2022 SIGKDD Conference on Knowledge Discovery and Data Mining.
Dr. Zheng Xu is a research scientist working on federated learning and privacy at Google. He got his Ph.D. in optimization and machine learning from University of Maryland, College Park. Before that, he got his master's and bachelor's degree from University of Science and Technology of China.
Dr. Zheng Xu is a research scientist working on federated learning and privacy at Google. He got his Ph.D. in optimization and machine learning from University of Maryland, College Park. Before that, he got his master's and bachelor's degree from University of Science and Technology of China.
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