摘要：
I will discuss some recent results on how higher-order interactions shape collective dynamics. For example, do higher-order interactions promote synchronization? What role does the hypergraph structure play? In the other direction, I will discuss how to infer hypergraphs from time-series data and demonstrate its applications in neuroscience. For example, how important are higher-order interactions in the brain? How do we know when are network models adequate and when are they not?
报告人简介：
Dr. Zhang is an Omidyar Fellow at the Santa Fe Institute. Before that, he was a Schmidt Science Fellow at Cornell working with Steven Strogatz. He received his Ph.D. in Physics from Northwestern University and his B.Sc. in Mathematics from Zhejiang University. His research focuses on developing mathematical and computational tools for the study of complex systems, especially using techniques from dynamical systems, network theory, data science, and machine learning. He applies these tools to understand the behavior of complex systems that are often nonlinear and high-dimensional, which includes questions in mathematical biology (how do circadian clocks re-synchronize as we recover from jet lag?), neuroscience (how important are nonpairwise interactions in shaping macroscopic brain dynamics?), data-driven modeling (when can digital twins generalize to previously unseen conditions?), and scientific machine learning (is zero-shot forecasting of chaotic systems possible?). Some topics he worked on recently include high-dimensional basins in multistable systems, dynamics and inference on higher-order networks, and out-of-distribution generalization in neural networks.