Yu Wang is an Assistant Professor at the Department of Computer Science at the University of Oregon, which conducts research in network analysis, graph machine learning, large language models for social-good applications. He received the Best Paper Award in the 2020 Smokey Mountain Data Challenge Competition by ORNL and GLFrontiers Workshop at Neurips'23, Best Doctoral Forum Poster Runner-ups at SDM'24, and Outstanding Reviewer at ECML-PKDD. He actively contributed to the community, both in publishing and serving as a PC member/reviewer/organizer, such as ICLR, NeurIPS, AAAI, KDD, WWW, CIKM, WSDM, TKDD, NAACL and TIST. He has contributed to organizing Machine Learning on Graph workshop in WSDM'22/24, delivered tutorials on Data quality-aware Graph Machine Learning on SDM'24/CIKM'24 and Graph Retrieval-augmented Generation at SDM'25, and served as the student travel award chair in CIKM'24.

Yu Zhang is an Assistant Professor at the Department of Computer Science & Engineering at Texas A&M University. He received his Ph.D. degree from the University of Illinois at Urbana-Champaign, advised by Prof. Jiawei Han. His research interests include data mining and NLP for science, text mining with graphs and structured knowledge, as well as text mining under weak supervision. Yu is the recipient of the UIUC Dissertation Completion Fellowship and the Yunni & Maxine Pao Memorial Fellowship. He has also been awarded the WWW 2018 and SDM 2023 Best Poster Award Honorable Mention. He has published over 40 conference and journal papers, given 8 conference tutorials, and served as an Area Chair or Reviewer for top venues such as KDD, WWW, WSDM, ICML, NeurIPS, ICLR, TKDE, TIST, TKDD, and ACL Rolling Review.

Zhichun Guo is a postdoctoral researcher in Prof. David Baker's lab at the University of Washington. Starting Fall 2025, she will join the Department of Computer Science at Emory University as a tenure-track assistant professor. Her research focuses on advancing graph neural networks (GNNs) for real-world applications, particularly in chemistry and biochemistry, while also tackling fundamental challenges in GNNs. She has authored numerous publications and served as a program committee member for top-tier venues, including KDD, ICLR, NeurIPS, ICML, WWW, and AAAI, among others. Her work has had a significant impact on the AI for Science community and has contributed to practical applications in industry.

Tyler Derr is an Assistant Professor in the Department of Computer Science at Vanderbilt University (VU), where he directs the Network and Data Science Lab. His research focuses on social network analysis, graph machine learning, responsible data science, and interdisciplinary applications for social good. He is actively involved in top conferences in his field, both in terms of publishing and serving as an AC/SPC/PC member, earning distinctions such as the having his work selected among the top-10 Most Influential Papers by Paper Digest at CIKM (2022) and WWW (2023), Best Student Poster Award at SDM (2019), and 3 Best Reviewer Awards. He has co-organized 10 workshops, 4 tutorials, and delivered over 40 invited talks. He has served on 10 conference organizing committees (e.g., for KDD and WSDM) and serves as an Associate Editor for five journals, including IEEE Transactions on Big Data. He has received several prestigious honors, including the NSF CAREER Award (2023) and selection for the Visiting Faculty Research Program at the AFRL/RI (2023). Additionally, he was honored with the Fall 2020 Teaching Innovation Award from the VU School of Engineering and the 2024 Provost Immersion Grant for Faculty at VU, highlighting his dedication to exceptional teaching and mentoring.

Nesreen Ahmed is a principal scientist at Cisco AI, where she leads a team of researchers working on large-scale machine learning and generative AI for networking and security applications. Previously, she was a senior scientist and Technical Lead at Intel Labs, driving AI-for-systems research in code generation, code translation, high-performance computing (HPC), and compiler optimization. She holds a Ph.D. in Computer Science and an M.S. in Statistics from Purdue University. Her research focuses on large-scale machine learning, statistical relational learning, and deep learning, with applications in personalization, recommendation, and code generation. She has held leadership roles in top AI/ML conferences, including serving as the PhD Consortium Chair at SIGKDD 2023, the Demo Track Chair at CIKM 2022, and the Program Committee Chair of IEEE Big Data 2018. Since 2020, she has been an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems. She was also a visiting researcher at Facebook, Adobe Research, and Technicolor. Dr. Ahmed has authored numerous papers and tutorials in top-tier conferences and journals and holds several U.S. patents. Her research has been featured in MIT Technology Review. She was selected as a Rising Star in Computer Science and Engineering by UC Berkeley in 2014 and received Intel’s Recognition Award in 2019 for her contributions to the successful DARPA HIVE proposal on large-scale graph analytics.

Jiliang Tang is a 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 organizer (e.g., KDD, SIGIR, WSDM, and SDM) and journal editor (e.g., TKDD, TOIS, and TKDE). He has organized 20+ workshops in top AI conferences such as AI for Education in AAAI20, AAAI21 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 38,000 citations with h-index 95 and extensive media coverage.