Nisheeth Vishnoi Gives 2/13 DSI Distinguished Lecture
Tuesday, February 13, 2018
11:00am – 12:15pm, refreshments & networking at 10:30am
Hariri Institute for Computing, Seminar Room
The Institute is pleased to host a Data Science Initiative (DSI) Distinguished Lecture, given by Nisheeth Vishnoi.
Professor, School of Computer and Communication Sciences
École Polytechnique Fédérale de Lausanne (Switzerland)
Algorithms, Nature and Society
Abstract: Algorithms are deeply embedded in the world around us — both natural and artificial. On the one hand, the language of algorithms is increasingly being used to describe and understand various processes in nature and society. On the other hand, our lives are being touched by powerful algorithm capable of learning about our behavior and nudging it. Understanding algorithms in nature and designing algorithms for society, tasks that may sometimes be interdependent, poses great challenges. Surprisingly, such studies can sometimes also lead to developments at the core of algorithms and optimization. I will present some vignettes from my research on such algorithmic interactions: a connection between slime mold dynamics and iteratively reweighted least squares method, a sampling algorithm inspired from Hamiltonian dynamics, and algorithms to control bias in data summarization.
Bio: Nisheeth Vishnoi is a professor in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne. His research focuses both on foundational problems in algorithms, complexity and optimization, and on how computation can be used to gain insight into processes in nature and society. He is the recipient of the Best Paper Award at FOCS 2005, the IBM Research Pat Goldberg Memorial Award for 2006, the Indian National Science Academy Young Scientist Award for 2011 and the IIT Bombay Young Alumni Achievers Award for 2016. He is an associate of the International Center for Theoretical Sciences, Bangalore. Prior to joining EPFL, he held positions at Microsoft Research, the Simons Institute for the Theory of Computing, CNRS, UC Berkeley and IBM Research.