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BIMSA Topology Seminar
BIMSA Topology Seminar
Topology-Guided Machine Learning for Spatio-Temporal Data and Beyond
Topology-Guided Machine Learning for Spatio-Temporal Data and Beyond
组织者
演讲者
Yuzhou Chen
时间
2026年03月26日 13:00 至 14:00
地点
A3-4-301
线上
Zoom 518 868 7656
(BIMSA)
摘要
In recent years, artificial intelligence (AI) has emerged as a new powerful machinery that enables us to harness the rich information encoded in complex real-world phenomena, from climate sciences to the spread of infectious diseases. However, the existing models still tend to be limited in their capabilities to explicitly account for higher-order relations within the encoded knowledge, especially in conjunction with multi-granular and multimodal datasets which are the inherent characteristics in various real-world applications. In this talk, I will introduce the emerging approaches of topological machine learning (TML), i.e., the new arsenal of tools at the interface of statistical topological data analysis, computational topology, machine learning (ML), and data science, and demonstrate how TML can help in addressing fundamental knowledge gaps on the way of the more systematic, reliable, and trustworthy applications of AI in intelligent transportation, biosurveillance, climate science, and bioinformatics. In particular, I will showcase the applications of our new TML machinery to the challenging problems in digital finance, intelligent transportation, biosurveillance of infectious diseases, and molecular graph analysis.
Dr. Yuzhou Chen is a tenure-track Assistant Professor in the Department of Statistics at UC Riverside. He is also a cooperating faculty in the Department of Electrical and Computer Engineering at UC Riverside, an adjunct professor in the Department of Computer and Information Sciences at Temple University, and a Visiting Research Collaborator in Department of Electrical and Computer Engineering at Princeton University. Before that, Dr. Chen worked as a postdoctoral scholar in the Department of Electrical and Computer Engineering at Princeton University. Dr. Chen received his Ph.D. in Statistics from Southern Methodist University. His research focuses on geometric deep learning, topological data analysis, knowledge discovery in graphs and spatio-temporal data, with applications to power systems, biosurveillance and environmental data analytics. His research has appeared in the top machine learning and data mining top conferences, including ICML, ICLR, NeurIPS, KDD, AAAI, etc. He was the recipient of 2025 UCR Regents Faculty Fellowship, 2025 Leonard Transportation Center Research Faculty Fellowship, 2024 American Statistical Association on Joint Statistical Computing and Statistical Graphics Section Best Student Paper Award, 2021/2022 American Statistical Association Section on Statistics in Defense and National Security Best Student Paper Award, and 2021 Chateaubriand Fellowship from the Embassy of France in the United States.
Dr. Yuzhou Chen is a tenure-track Assistant Professor in the Department of Statistics at UC Riverside. He is also a cooperating faculty in the Department of Electrical and Computer Engineering at UC Riverside, an adjunct professor in the Department of Computer and Information Sciences at Temple University, and a Visiting Research Collaborator in Department of Electrical and Computer Engineering at Princeton University. Before that, Dr. Chen worked as a postdoctoral scholar in the Department of Electrical and Computer Engineering at Princeton University. Dr. Chen received his Ph.D. in Statistics from Southern Methodist University. His research focuses on geometric deep learning, topological data analysis, knowledge discovery in graphs and spatio-temporal data, with applications to power systems, biosurveillance and environmental data analytics. His research has appeared in the top machine learning and data mining top conferences, including ICML, ICLR, NeurIPS, KDD, AAAI, etc. He was the recipient of 2025 UCR Regents Faculty Fellowship, 2025 Leonard Transportation Center Research Faculty Fellowship, 2024 American Statistical Association on Joint Statistical Computing and Statistical Graphics Section Best Student Paper Award, 2021/2022 American Statistical Association Section on Statistics in Defense and National Security Best Student Paper Award, and 2021 Chateaubriand Fellowship from the Embassy of France in the United States.