BEGIN:VCALENDAR
PRODID:-//eluceo/ical//2.0/EN
VERSION:2.0
CALSCALE:GREGORIAN
BEGIN:VEVENT
UID:www.tcs.tifr.res.in/event/758
DTSTAMP:20230914T125937Z
SUMMARY:Learning Inhibition Graphs Through Boolean Compressive Sensing
DESCRIPTION:Speaker: Abhinav Ganesan (Senior Engineer\nQualcomm R&D - Modem
  Systems\nBangalore)\n\nAbstract: \nDesign of efficient sampling methodolo
 gies to capture the information content in sparse\, and thus\, "compressib
 le" signals is well known as compressive sensing (CS) in the signal proces
 sing community. These efficient sampling methods that use linear measureme
 nts (tests) of signals are known to have significantly low sampling cost c
 ompared to naive sampling. An analogous\, albeit a non-linear paradigm\, c
 alled group testing emerged much before the compressive sensing era. Boole
 an Compressive Sensing (BCS) a.k.a Group Testing was introduced by Dorfman
  in 1943 to identify a small number of unhealthy individuals (i.e.\, defec
 tive "items") amidst a large population. In the BCS model\, the test outco
 mes are a non-linear function of the input\, and the input and the output 
 vectors are binary. The addition and multiplication operations in CS are r
 eplaced by Boolean-OR and Boolean-AND operations. The BCS framework has si
 nce been used in diverse fields like Bioinformatics\, Theoretical Computer
  Science\, Computer Networks\, Sports Analytics\, and Recreational Mathema
 tics.\n\nThis talk will focus on a newly introduced variation of the class
 ical BCS framework that will model a unified problem of pathogen identific
 ation and drug discovery. The expression of a pathogen carrying item is in
 hibited by blocker items in a "pooled" test. Such an activity relationship
  between the items is modelled as a bipartite graph\, called an inhibition
  graph. The goal is to learn this unknown inhibition graph with least samp
 ling complexity. Upper bounds on the sampling complexity and tight non-tri
 vial lower bounds will be discussed for learning the inhibition graph thro
 ugh "non-adaptive" BCS.\n\nThe talk will also highlight a few other topics
  that I have worked on and my future research plans.\n\nBiography: Abhinav
  Ganesan is a Senior Systems Engineer at Qualcomm R&D\, Bangalore\, workin
 g on LTE system design for IoT applications. He was a Post Doctoral Fellow
  at the Institute of Network Coding\, The Chinese University of Hong Kong\
 , during 2014-2015. He obtained his PhD from the ECE Department at the Ind
 ian Institute of Science in 2014 and his B.E. degree from the College of E
 ngineering\, Guindy\, Anna University in 2009. He was a recipient of the B
 est Academic Paper Award at the IEEE Wireless Communications and Networkin
 g Conference (WCNC) in 2011.\n \n
URL:https://www.tcs.tifr.res.in/web/events/758
DTSTART;TZID=Asia/Kolkata:20170303T153000
DTEND;TZID=Asia/Kolkata:20170303T163000
LOCATION:A-201 (STCS Seminar Room)
END:VEVENT
END:VCALENDAR