Award talks
Prof. Philip S. Yu
University of Illinois at Chicago, USA
Title: Riemannian Geometric Deep Graph Learning: A New Perspective on Graph Foundation Models
Abstract
Graph is a ubiquitous non-Euclidean structure, describing the intercorrelated objects in the complex system, ranging from social networks, transportation systems, financial transaction networks to biochemical and molecule structures. Nowadays, graph neural networks are becoming the de facto solution for learning on graphs, generating node or graph embeddings in representation space, such as the traditional Euclidean space. However, a natural and fundamental question that has been rarely explored is: which representation space is more suitable for complex graphs? In fact, the "flat" Euclidean space is suitable for grid structures but is not geometrically aligned with generic graphs with complex structures. Thus, it is interesting to explore deep graph learning in different geometric spaces. This talk will delve into the fascinating properties of mixing various geometric spaces (e.g., hyperbolic and hyperspherical spaces), grounded in the elegant framework of Riemannian geometry, and will discuss recent advancements in tasks such as classification, clustering, contrastive learning, graph structure learning, and continual graph learning. With graph foundation models drawing increasing attention, the talk will also cover preliminary work on building a foundation model for graph structures by exploring mixed geometric spaces. These endeavors pave the way for the next generation of deep graph learning.
Bio
Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. He is a Fellow of the ACM and IEEE.
Bio
Dr. Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. He is a Fellow of the ACM and IEEE. Dr. Yu is the recipient of ACM SIGKDD 2016 Innovation Award for his influential research and scientific contributions on mining, fusion and anonymization of big data, the IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data” and the Research Contributions Award from ICDM in 2003 for his pioneering contributions to the field of data mining. Dr. Yu has published more than 1,700 referred conference and journal papers cited more than 217,000 times with an H-index of 204. He has applied for more than 300 patents. Dr. Yu was the Editor-in-Chiefs of ACM TKDD (2011-2017) and IEEE TKDE (2001-2004).
Assoc. Prof. Evangelos (Vagelis) Papalexakis
University of California, Riverside, USA
Title: Reflections on the past, present, and future of tensor methods in data mining
Abstract
Tensor methods have been indispensable and transformative tools in data mining, machine learning, and artificial intelligence. Whether directly applied towards knowledge discovery, or indirectly providing efficiency and robustness to a large deep model, tensor methods and low-rank methods in general have had a profound impact and are here to stay.
In this talk, I will provide my reflections on over a decade of research in tensor methods for data mining and machine learning. Our journey will begin from earlier work on scalable tensor methods, which laid the foundations for truly large-scale mining of heterogeneous datasets. We will continue with a number of key applications, including ones in scientific and knowledge discovery, where tensor methods have made a significant difference. Subsequently, we will visit the different ways in which tensor methods are currently interacting with deep models and large language models, including robustness against adversaries. Finally, we will conclude our journey with the future outlook of tensor methods in tomorrow's AI-dominated research and practice.
Bio
Evangelos (Vagelis) Papalexakis is an Associate Professor and the Ross Family Chair of the CSE Dept. at University of California Riverside. He received his PhD degree at the School of Computer Science at Carnegie Mellon University (CMU).
Bio
Evangelos (Vagelis) Papalexakis is an Associate Professor and the Ross Family Chair of the CSE Dept. at University of California Riverside. He received his PhD degree at the School of Computer Science at Carnegie Mellon University (CMU). Prior to CMU, he obtained his Diploma and MSc in Electronic & Computer Engineering at the Technical University of Crete, in Greece. Broadly, his research interests span the fields of Data Science, Machine Learning, Artificial Intelligence, and Signal Processing.
His research involves designing interpretable models and scalable algorithms for extracting knowledge from large multi-aspect datasets, with specific emphasis on tensor factorization models, and applying those algorithms to a variety of real-world problems, including detection of misinformation on the Web, explainable AI, gravitational wave detection, cybersecurity, transportation and railway safety, and precision agriculture.
He is heavily involved in the data science research community with extensive experience in conference organization, including organizing a workshop at ACM SIGKDD 2019 on "Tensor Methods for Emerging Data Science Problems", being the Deep Learning Day Co-Chair for ACM SIGKDD 2019, the Doctoral Forum Co-Chair for SIAM SDM 2021, the Demos Co-Chair for ACM WSDM 2022, the Program Co-Chair for SIAM SDM 2022, and the General Co-Chair for SIAM SDM 2024 and SIAM SDM 2025.
His work has appeared in top-tier conferences and journals, and has attracted a number of distinctions, including the 2017 SIGKDD Dissertation Award (runner-up), a number of paper awards, the National Science Foundation CAREER award, the 2021 IEEE DSAA Next Generation Data Scientist Award, the 2022 IEEE Signal Processing Society Donald G. Fink Overview Paper Award, and the IEEE ICDM 2022 Tao Li Award and 2025 PAKDD Early Career Research Award, both of which award excellence in early-career researchers in data mining.