Abstract:
Spatio-temporal event data play a central role in many modern scientific and societal applications, including urban mobility, public health, financial systems, and blockchain ecosystems. Such data are inherently relational, dynamically evolving, and non-Euclidean in nature, which poses fundamental challenges for classical statistical models and standard deep learning approaches. In this talk, I will introduce novel topology-guided machine learning frameworks that integrate spatio-temporal modeling, graph representation learning, and topological data analysis to address these challenges in a principled manner. First, I introduce a topology-enhanced diffusion framework for spatio-temporal point processes, which captures complex event dependencies through joint spatio-temporal graph construction and topological representations and leads to improved predictive performance and interpretability. Second, I discuss a multilayer topology-aware graph contrastive learning framework for fraud detection in time-evolving blockchain transaction networks, where persistent homology is employed to encode higher-order structural patterns beyond pairwise interactions. Together, these works demonstrate how topological structure can serve as a unifying analytical lens for learning from complex spatio-temporal data, and offer both practical performance gains and deeper statistical insight into the organization and dynamics of real-world complex systems.
Bio: 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 and energy systems, biosurveillance, finance data, 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.
Abstract:
Spatio-temporal event data play a central role in many modern scientific and societal applications, including urban mobility, public health, financial systems, and blockchain ecosystems. Such data are inherently relational, dynamically evolving, and non-Euclidean in nature, which poses fundamental challenges for classical statistical models and standard deep learning approaches. In this talk, I will introduce novel topology-guided machine learning frameworks that integrate spatio-temporal modeling, graph representation learning, and topological data analysis to address these challenges in a principled manner. First, I introduce a topology-enhanced diffusion framework for spatio-temporal point processes, which captures complex event dependencies through joint spatio-temporal graph construction and topological representations and leads to improved predictive performance and interpretability. Second, I discuss a multilayer topology-aware graph contrastive learning framework for fraud detection in time-evolving blockchain transaction networks, where persistent homology is employed to encode higher-order structural patterns beyond pairwise interactions. Together, these works demonstrate how topological structure can serve as a unifying analytical lens for learning from complex spatio-temporal data, and offer both practical performance gains and deeper statistical insight into the organization and dynamics of real-world complex systems.
Bio: 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 and energy systems, biosurveillance, finance data, 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.