CSL Communications Group Calendar
http://illinois.edu/calendar/list/3123
CSL Communications Group CalendarCommunications/ICWS Seminar - “On generalizing common information with interactive function computation in mind”
http://illinois.edu/calendar/detail/3123/32051918
http://illinois.edu/calendar/detail/3123/32051918Thu, 28 Aug 2014 16:00:00 CDT***Abstract***
Gács & Körner (1973) and Wyner (1975) gave two different definitions for the "common information" of a pair of random variables motivated by two different views of how to define what is "common" between the pair. In this talk I will present a generalization of these concepts. The motivation is to better understand the communication requirements for interactive function computation, both with and without security.
Based on joint works with Deepesh Data (TIFR), Manoj Prabhakaran (UIUC), and Sankeerth Rao (IIT, Bombay).
*****Bio*****
Vinod M. Prabhakaran received his Ph.D. in 2007 from the University of California, Berkeley. He was a Postdoctoral Researcher at the Coordinated Science Laboratory, UIUC from 2008 to 2010 and at EPFL in 2011. Since 2011, he has been at Tata Institute of Fundamental Research (TIFR), Mumbai. His research interests are in information theory and cryptography.**SPECIAL** Communications/ICWS Seminar - “Probing networks to understand nature”
http://illinois.edu/calendar/detail/3123/32051912
http://illinois.edu/calendar/detail/3123/32051912Thu, 11 Sep 2014 15:00:00 CDT***Abstract***
Networks are a fundamental tool for understanding the intricate interconnections that govern biological systems. This talk will describe two ways in which networks, in combination with mathematical models and algorithmic techniques, can yield valuable biological insights.
Causal regulatory networks help reveal the hidden regulators of gene expression patterns. To facilitate their analysis we established an efficient method for evaluating the significance of the overlap of ternary signals, which generalizes Fisher's exact test. We used this method to analyze a large-scale causal regulatory network and uncovered new regulators of cardiac hypertrophy.
Metabolic networks help identify novel drug targets. We uncovered structural features of these networks that had been missed by previous researchers, and developed a theoretical framework based on duality for analyzing them in a consistent fashion. We used this theoretical framework to create a new metabolic network for Mycobacterium tuberculosis by algorithmically merging two existing networks, and identified several putative drug targets.
*****Bio*****
Leonid earned a Bachelor's degree in Mathematics and Computer Science from McGill University and his PhD in Applied Mathematics from MIT, with a thesis entitled "Extracting Information from Biological Networks". He is currently finishing a postdoctoral fellowship at the Harvard School of Public Health, where he works in a collaborative group on mathematical models of tuberculosis.