Organizer

Wei Jin is an Assistant Professor of Computer Science at Emory University. He obtained his Ph.D. from Michigan State University in 2023. His research focuses on data-centric AI and trustworthy AI for graph data, with notable accomplishments such as Snap Research Fellowship, Most Influential Papers in KDD and WWW by Paper Digest, SDM Best Poster Award Honorable Mention, 3rd Place of Fitch H. Beach Award, and top finishes in three NeurIPS competitions. He has organized tutorials and workshops at KDD, AAAI, and SDM, and published in top-tier venues such as ICLR, KDD, ICML, and NeurIPS. He has served as (senior) program committee members at these conferences and received the WSDM Outstanding Program Committee Member award. He also organized the KDDCup Workshop at KDD’23.

Haohan Wang is an assistant professor in the School of Information Sciences at the University of Illinois Urbana-Champaign. His research focuses on the development of trustworthy machine learning methods for computational biology and healthcare applications, such as decoding the genomic language of Alzheimer’s disease. In his work, he uses statistical analysis and deep learning methods, with an emphasis on data analysis using methods least influenced by spurious signals. Wang earned his PhD in computer science through the Language Technologies Institute of Carnegie Mellon University. In 2019, Wang was recognized as the Next Generation in Biomedicine by the Broad Institute of MIT and Harvard because of his contributions in dealing with confounding factors with deep learning.

Yao Ma is an Assistant Professor in the Department of Computer Science at the Rensselaer Polytechnic Institute (RPI). Before joining RPI, he worked as an Assistant Professor at the New Jersey Institute of Technology (NJIT) for two years. He received his Ph.D. in Computer Science from Michigan State University (MSU) in 2021, with a focus on machine learning with graph-structured data. His research contributions to this area have led to numerous innovative works presented at top-tier conferences such as KDD, WWW, WSDM, ICLR, NeurIPS, and ICML. He has also organized and presented several well-received tutorials at AAAI and KDD, attracting over 1000 attendees. He is the author of the book “Deep Learning on Graphs”, which has been downloaded tens of thousands of times from over 100 countries. He was awarded the Outstanding Graduate Student Award (2019-2020) from the College of Engineering at MSU. He has also organized four workshops at ICDM, WSDM, and SDM.

Daochen Zha is a machine learning engineer. He received his Ph.D. from Rice University. His research focuses on reinforcement learning and its applications in data-centric AI. His work has appeared at top-tier venues, such as WWW, KDD, WSDM, ICML, NeurIPS, and ICLR, and won the Best Demo Paper Award in CIKM’22. He leads several open-sourced libraries with more than 9,000 GitHub stars in total.

Qiaoyu Tan is an Assistant Professor in the Computer Science Department at New York University Shanghai. He earned his PhD in Computer Science and Engineering Department from Texas A&M University (TAMU) in 2023. His research research centers on the fields of machine learning and data mining, with a particular focus on graph learning, foundation models, multi-modality learning, and their applications in bioinformatics and healthcare. His work has been awarded AMIA’23 Best Student Paper Finalist.

Sharon Li is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin Madison. Previously she was as a postdoc researcher in the Computer Science department at Stanford University, working with the Hazy Group. She completed her PhD from Cornell University in 2017, where she was advised by John E. Hopcroft. Her research aims at bringing AI systems to the level where they are not only intelligent, but also safe and beneficial for humanity. She has received numerous awards including Innovator of the Year by MIT Technology Review, NSF CAREER Award, AFOSR Young Investigator Program (YIP) Award, NeurIPS’22 Outstanding Paper Award, and ICLR Outstanding Paper Award Honorable Mention. She has co-organized multiple workshops at ICML and NeurIPS.

Su-In Lee is a Paul G. Allen Professor of Computer Science & Engineering at the UW. She completed her PhD in 2009 at Stanford University with Prof. Daphne Koller in the Stanford Artificial Intelligence Laboratory in Computer Science. Before joining the UW in 2010, Lee was a visiting Assistant Professor in the Computational Biology Department at Carnegie Mellon University School of Computer Science. She has received the National Science Foundation CAREER Award and has been named an American Cancer Society Research Scholar. She has received numerous generous grants from the National Institutes of Health (NIH), the National Science Foundation (NSF), and the American Cancer Society.