Special CSL Social Hour, September 23, 2016

The CSL Social Hour Organizing Committee is very excited to announce this week’s special social hour by CSL professors – Prof. Maxim Raginsky and Prof. Alex Schwing. They will provide an overview of SINE (Signals, Inference, and Networks) group activities. The event will be held on Friday, September 23 at 3:00PM in CSL 369.

maxBio: Maxim Raginsky received the B.S. and M.S. degrees in 2000 and the Ph.D. degree in 2002 from Northwestern University, all in Electrical Engineering. He has held research positions with Northwestern, the University of Illinois at Urbana-Champaign (where he was a Beckman Foundation Fellow from 2004 to 2007), and Duke University. In 2012, he has returned to the UIUC, where he is currently an Assistant Professor with the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory.

Prof. Raginsky is interested in understanding, modeling and analyzing complex systems that have capabilities for sensing, communication, adaptation, and decision-making and can operate effectively in uncertain and dynamic environments. In his research he examines new angles and perspectives at the interface between information theory, learning, optimization, and control.



BioAlex Schwing is an assistant professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign and affiliated with the Coordinated Science Lab. Prior to that he was a postdoctoral fellow in the Machine Learning Group at the University of Toronto. He completed his PhD in computer science in the Computer Vision and Geometry Group at ETH Zurich. He graduated from Technical University of Munich (TUM) with a diploma in Electrical Engineering and Information Technology.

Prof. Schwing’s research is centered around machine learning and computer vision. He is particularly interested in algorithms for prediction with and learning of non-linear, multivariate and structured distributions, and their application in numerous tasks, e.g., for 3D scene understanding from a single image.