Multiway Tensor Analysis with Neuroscience Applications
Experimental advances in neuroscience enable the acquisition of increasingly large-scale, high-dimensional and high-resolution neuronal and behavioral datasets, however addressing the full spatiotemporal complexity of these datasets poses significant challenges for data analysis and modeling. We propose to model such datasets as multiway tensors with an underlying graph structure along each mode, learned from the data. In this talk I will present three frameworks we have developed to model, analyze and organize tensor data that infer the coupled multi-scale structure of the data, reveal latent variables and visualize short and long-term temporal dynamics with applications in calcium imaging analysis, fMRI and artificial neural networks.
Gal is an assistant professor in the Halıcıoğlu Data Science Institute (HDSI) at UC San Diego, and affiliated with the ECE department and the Neurosciences Graduate program. I am part of the Neurotheory Network. Before arriving at UCSD, I was a Gibbs Assistant Professor in the Applied Math program at Yale University, with Prof. Ronald Coifman's research group. I completed my PhD in 2017 at the Technion at the Faculty of Electrical Engineering under the supervision of Prof. Israel Cohen.