Talk Title: Interpretable Modeling of Neural Event Propagation in Brain Networks

Abstract: Neural activity can be thought of as a stream of discrete events unfolding over time across interconnected brain networks. In this talk, I will present a new spatiotemporal point process framework for modeling how these events influence one another and propagate through the network. The model brings together network structure, time-varying dynamics, and basic excitatory and inhibitory interactions. Beyond improving prediction, the framework provides an interpretable way to explore neural activity, helping reveal patterns of interaction and information flow in the brain. I will also discuss applications, including modeling how activity spreads in neurological disorders.

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Bio: Rahul joined the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island as an Assistant Professor in Fall 2025. Prior to that, he was a Postdoctoral Associate at Yale University. Rahul received his Ph.D. in Machine Learning from Georgia Tech in 2023, where he focused on learning for graph-structured data. His research interests are in the areas of Machine Learning, Signal Processing, and Computational Neuroscience.