Princeton University

School of Engineering & Applied Science

Causal Inference in Stochastic Networks: Going Beyond Linear Models

Negar Kiyavash, University of Illinois at Urbana-Champaign
Engineering Quadrangle B205
Thursday, September 26, 2013 - 3:30pm to 4:30pm


Directed information graphs are a new type of probabilistic graphical model based on directed information that represent the casual dynamics among random processes in a stochastic systems. In this talk, we present a framework for learning the structure of such graphs which goes beyond linear models studied in the literature. Additionally, in the presence of large data, we propose algorithms that identify optimal or near optimal approximations to the topology.


Negar Kiyavash holds a B.Sc. from the Sharif University of Technology, Tehran (in 1999), M.S. and Ph.D. degrees from University of Illinois at Urbana-Champaign (in 2003 and 2006, respectively), all in Electrical and Computer Engineering. From 2006 through 2008, she was a Research Faculty at Department of Computer Science and a Research Scientist at Information Trust Institute at the University of Illinois at Urbana-Champaign. Since 2009 she has been an Assistant Professor of the Department of Industrial and Enterprise Engineering, an Affiliate Assistant Professor of Electrical and Computer Engineering, and a Research Assistant Professor of Coordinated Science laboratory (CSL).

Negar Kiyavash is a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE). She is a recipient of the National Science Foundation Career award and the Air Force Office of Scientific Research Young Investigator Program ward.

Negar Kiyavash's research interests are in statistical signal processing, information theory, and analysis of time series with applications to complex networks and security.