Data Science Talk Series 

Title: Brain and AI

Abstract: Studying the nature of biological neural networks (BNN), or brain connectomics, in the brain has been a frontier in neuroscience. In parallel, designing and optimizing artificial neural networks (ANN) is a core research topic in deep learning and artificial intelligence (AI). Despite that ANN was originally inspired by BNN in the early stage, there has been little synergy or interaction between the fields of BNN and ANN research due to the significant gaps between these two domains. In this talk, I will present our discoveries aiming to bridge these two domains, and jointly raise the expectations of both brain connectomics and ANN fields. In particular, I will introduce our recent work about using AI to better understand brain connectomics as well as leveraging the principles found in BNN to guide and improve the ANN architecture design.

Bio of the speaker: Dr. Dajiang Zhu is an Associate Professor in the Department of Computer Science & Engineering at the University of Texas at Arlington (UTA). Dr. Zhu received his Ph.D. in Computer Science from the University of Georgia in 2014. Before he joined UTA, Dr. Zhu was a Post-Doctoral Scholar in the Imaging Genetics Center at the University of Southern California. His research focuses on Neuroimaging Computing and Brain-inspired AI and has published 160+ papers at top-tier conferences and journals. He is a recipient of the “Rising STARs award” of the University of Texas and his research is supported by multiple NIH R01s.