Tuesday, November 15, 2016 - 4:00pm
Event Calendar Category
LIDS Seminar Series
University of Illinois Urbana-Champaign
Building and Room Number
Learning the influence structure of multiple time series data is of great interest to many disciplines. We discuss approaches for learning causal interaction network of mutually exciting Hawkes processes. In such processes, the arrival of an event in one process increases the probability of occurrence of new events in some of the other processes. Thus, a natural notion of causality exists between the processes. In this talk, we first show that the causal interaction network learned from the above natural definition is equivalent to the Directed Information (DI) graph of the processes. DI graphs are a class of graphical models, which are well suited to capture causal interactions in times series data. We discuss some interesting use cases of the algorithm for analyzing stock market datasets as well as clustering websites based on hyperlink information from news media articles and blog posts.
Negar Kiyavash is Willett Faculty Scholar at the University of Illinois and a joint Associate Professor of Industrial and Enterprise Engineering and Electrical and Computer Engineering. She is also affiliated with the Coordinated Science Laboratory (CSL) and the Information Trust Institute. She received her Ph.D. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign in 2006. Her research interests are in design and analysis of algorithms for network inference and security. She is a recipient of NSF CAREER and AFOSR YIP awards and the Illinois College of Engineering Dean's Award for Excellence in Research.