Date: 2019-10-25

Time: 15:30-16:30

Location: BURN 1205

Abstract:

High-dimensional point processes have become ubiquitous in many scientific fields. For instance, neuroscientists use calcium florescent imaging to monitor the firing of thousands of neurons in live animals. In this talk, I will discuss new methodological, computational and theoretical developments for learning neuronal connectivity networks from high-dimensional point processes. Time permitting, I will also discuss a new approach for handling non-stationarity in high-dimensional time series.

Speaker

Ali Shojaie is an Associate Professor of Biostatistics, and Adjunct Associate Professor of Statistics in the Department of Biostatistics at the University of Washington. His research focuses on statistical machine learning, high-dimensional networks, estimation and inference in high-dimensional problems.