Organizers
Meet the Organizers
Haoyu Han
Michigan State University
Haoyu Han is a senior Ph.D. student of computer science and engineering at Michigan State University. His supervisor is Dr. Jiliang Tang. Before joining MSU, he completed his M.S. ~(2021) and B.S.~(2018) at University of Science and Technology of China. His current research interests include LLMs and machine learning on graphs. After joining MSU, he has published several works in top conferences (e.g., KDD, ICDM, NeurIPS, ICML, and ICLR). He was the recipients of the KDD'22, NeurIPS'24 Student Travel Award.
Fali Wang
The Pennsylvania State University
Fali Wang is a senior Ph.D. candidate in informatics at the Pennsylvania State University (PSU), under the guidance of Dr. Suhang Wang. Prior to joining PSU, he earned his M.S. degree in 2021 from UCAS and his B.E. in 2018 from NEFU. His research primarily focuses on LLM Knowledge and Graph Learning. Since enrolling at PSU, he has published several papers presented at top conferences (e.g., ACL, EMNLP, ICLR, CIKM, and WSDM).
Xianfeng Tang
Amazon
Senior Applied Scientist
Xianfeng Tang is a senior Applied Scientist at Amazon. His research mainly about machine learning, graph neural networks, data mining and natural language understanding. He obtained a PhD degree from the Pennsylvania State University, and a Bachelor degree from University of Science and Technology of China. He has published innovative works in top-tier conferences such as ICLR, ICML, NeurIPS, KDD, etc.
Hui Liu
Amazon
Applied Scientist
Hui Liu is an Applied Scientist at Amazon Search. He received his PhD from Queen’s University, Canada, and a Bachelor's degree from Peking University. His research mainly focuses on natural language processing, large language models, text mining, and machine learning. He has publications in top-tier conferences such as ACL, EMNLP, ICLR, etc.
Zhenwei Dai
Amazon
Applied Scientist
Zhenwei Dai is an applied scientist at Amazon Search. He received his PhD degree from Rice University and Bacheloar degree from Chinese University of Hong Kong (CUHK). His research mainly focus on information retrieval, machine learning, natural language processing and large language models. He has publications in top conferences such as NeurIPS, ICML, ICLR, SIGMOD, etc.
Jing Huang
Amazon
Principal Scientist
Jing Huang is a principal scientist at Amazon. She and her team work on advanced conversational AI technology, pushing the boundaries of human-machine interactions with a better understanding of context, commonsense reasoning, and personal preferences; and building multi-modal generative AI technology using LLMs. Previously, she worked as a senior director and senior principal research scientist at JD AI Research and Platforms (JD.com). She has more than 20 years of invaluable industry R&D experience with a focus on cutting-edge AI technologies. Her portfolio is highlighted by groundbreaking research and product development in areas including multi-modal content generation (Generative AI), natural language processing (Foundation Models/LLMs, question answering, information extraction, sentiment analysis), conversational AI, knowledge graph and recommendation, speech recognition, and deep learning (GNNs and DNNs). She organized the 2023 AAAI Workshop on "Creative AI Across Modalities". She currently serves on the IEEE SPS Speech and Language Processing Technical Committee, and has been an associate editor for IEEE/ACM Transactions on Audio, Speech and Language Processing since 2021.
Yiwei Sun
Meta GenAI
Software Engineer
Yiwei Sun works at applied research team at Meta GenAI. He received his PhD from Pennsylvania State University, and Bachelor's degree from Huazhong University of Science and Technology. His research mainly focuses on natural language processing, large language models, and machine learning. He has publications in top-tier conferences such as KDD, WWW, WSDM, etc.
Zhen Li
Amazon
Engineering Manager
Zhen Li is an Engineering Manager at Amazon Search, where he leads a team dedicated to enhancing customer conversation understanding to identify shopping intent. Before joining Amazon, Zhen spent several years at Facebook (now Meta), driving user growth and advertising initiatives. His expertise includes applying Graph Neural Network models for abuse detection and leveraging Large Language Models (LLMs) to innovate in e-commerce applications.
Chen Luo
Amazon
Senior Applied Scientist
Chen Luo is a senior applied scientist at Amazon Search, leading a team of scientists and engineers in query understanding and its applications in matching, ranking, and advertising. His research focuses on scalable machine learning for information retrieval and recommender systems. He received his Ph.D. from Rice University and has published in ML and IR conferences and journals such as WWW, KDD, SIGIR, AAAI, and JMLR. He regularly serves as SPC or PC for NeurIPS, ICML, KDD, AAAI, and WWW.
Dawei Yin
Baidu
Senior Director of Engineering
Dawei Yin is Senior Director of Engineering at Baidu inc.. He is managing the search science team at Baidu, leading Baidu’s science efforts of web search, question answering, video search, image search, news search, app search, etc.. Previously, he was Senior Director, managing the recommendation engineering team at JD.com between 2016 and 2020. Prior to JD.com, he was Senior Research Manager at Yahoo Labs, leading relevance science team and in charge of Core Search Relevance of Yahoo Search. He obtained Ph.D. (2013), M.S. (2010) from Lehigh University and B.S. (2006) from Shandong University. His research interests include data mining, applied machine learning, information retrieval and recommender system. He serves as conference organizers (e.g., KDD, SIGIR, WSDM) and journal editors (e.g., FnTIR). He published more than 100 research papers in premium conferences and journals, and was the recipient of 8 Best Paper Awards (or runner-ups), including KDD, WSDM, ICDM Best Paper Awards.
Suhang Wang
The Pennsylvania State University
Associated Professor
Suhang Wang is an Associate Professor of the College of Information Sciences and Technology at The Pennsylvania State University – University Park. His research interests are in graph mining, trustworthy machine learning, and generative artificial intelligence. He has published 150+ papers in top-tier machine learning and data mining conferences and journals, which has garnered 22,700+ Google Scholar citations with h-index 60. He is a recipient of the AI 2000 Most Influential Scholar Honorable Mention from AMiner and the 2022 Global Top Chinese Young Scholars in Artificial Intelligence. He has served as an area chair and senior PC member for many conferences such as KDD, NeurIPS, WSDM, and CIKM. He is also an associate editor for neurocomputing, ACM TKDD, ACM TIST, and Frontiers in Big Data.
Jiliang Tang
Michigan State University
University Foundation Professor
Jiliang Tang is University Foundation Professor in the computer science and engineering department at Michigan State University. His research interests include graph machine learning, trustworthy AI, and their applications in Education and Biology. He authored the first comprehensive book “deep learning on graphs” with Cambridge University Press and developed various well-received open-sourced tools including scikit-feature for feature selection, DeepRobust for trustworthy AI and DANCE for single-cell analysis. He was the recipient of various career awards (2022 IAPR J. K. AGGARWAL, 2022 SIAM SDM, 2021 IEEE ICDM, 2021 IEEE Big Data Security, 2020 ACM SIGKDD, 2019 NSF), numerous industrial faculty awards and 8 best paper awards (or runner-ups) including WSDM2018 and KDD2016. He serves as conference organizers (e.g., KDD, SIGIR, WSDM and SDM) and journal editors (e.g., TKDD, TOIS and TKDE). He has organized 20+ workshops in top AI conferences such as AI for Education in AAAI20, AAAI2021 Spring Symposium on Artificial Intelligence for K-12 Education, DLG-AAAI'21 and DLG-AAAI'23. He has published his research in highly ranked journals and top conference proceedings, which have more than 41,000 citations with h-index 98 and extensive media coverage.
Qi He
Amazon
Director of Applied Science
Qi He is a technical leader in AI and its business applications, with a track record of 20 years of experience leading and executing large complex AI projects. He serves as a Steering Committee member of ACM CIKM and an advisory board member of Neurocomputing Journal. He held many editorial and conference chair positions, including Associate Editor of IEEE TKDE and Neurocomputing Journal, General Chair of CIKM 2013, PC Chair of CIKM 2019 and Industry Chair of Web 2024, while also served as a (senior) program committee member of SIGKDD, SIGIR, WWW, CIKM, and WSDM for over a decade. Qi has published over 70 papers and patents with over 7000 citations to date. He received the 2008 ACM SIGKDD Best Application Paper Award and the 2020 ACM WSDM 10-year Test of Time Award. Qi is an IEEE Fellow, ACM Distinguished Member and was featured as the People of ACM in February 2021.
Jian Pei
Duke University
Professor and Chair, Department of Computer Science
Jian Pei is a Professor and Chair at Duke University, holding a joint position among Computer Science, Biostatistics and Bioinformatics, and Electric and Computer Engineering. He is a renowned researcher in data science, big data, data mining, and database systems. He is recognized as a Fellow of the Royal Society of Canada (i.e., the national academy of Canada), the Canadian Academy of Engineering, the Association of Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). At the same time, he is also renowned for his active and productive professional leadership. Jian Pei is one of the most cited authors in data mining, database systems, and information retrieval. Since 2000, he has published one textbook, two monographs and over 200 research papers in refereed journals and conferences, which have been cited over 130,000 times. He received many prestigious awards, including the 2017 ACM SIGKDD Innovation Award, the 2015 ACM SIGKDD Service Award, and the 2014 IEEE ICDM Research Contributions Award.