Invited Talk (CoTMA): Evangelos Papalexakis — Tensor Methods for Trustworthy & Robust AI Models

University of California, Riverside

Abstract: Tensors methods have been traditionally widely used in expressing complex relations in data and extracting interpretable latent insights. At the same time, more recently, tensor methods have been shown to represent model weights, with model compression through low-rank factorization being the most successful such application. In this talk we explore a number of exciting examples where tensor methods, whether applied directly on a model or indirectly in inputs or outputs of a model, can lead to more compact, trustworthy, and robust models.

Bio: Evangelos (Vagelis) Papalexakis is a Full 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 AI for Science and Engineering, 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, the General Co-Chair for SIAM SDM 2024 and SIAM SDM 2025, the Blue Sky Track Co-Chair for KDD 2026, and is currently serving as the Steering Committee Chair for SIAM SDM.

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. Finally, Prof. Papalexakis is a 2025 alumnus of the Frontiers of Engineering (FOE) Symposium by the National Academy of Engineering (NAE).