Abstract: In understanding physical systems over hundreds of years, physicists have developed a wealth of dynamics and viewpoints. Some of these methods, when abstracted appropriately, could lead to new algorithmic techniques with applications to machine learning and theoretical computer science. I will present a couple of recent examples from my own research on such interactions between Physics and Algorithms -- a Hamiltonian Dynamics inspired algorithm for sampling from continuous distributions and a Boltzmann's equation based algorithm for estimating the partition function for discrete distributions.
Bio: Nisheeth Vishnoi is currently a professor in the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne where he heads the theory of computation lab. Starting January 2019, he will a professor of Computer Science at Yale. He is also an associate of the International Center for Theoretical Sciences, Bangalore, an adjunct faculty member of IIT Delhi and IIT Kanpur, and a co-founder of the Computation, Nature, and Society ThinkTank in Lausanne. His research focuses on foundational problems in algorithms, optimization, and statistics, and how tools from these areas can be used to address computational questions in society and other sciences. Topics from these areas that he is currently interested in include algorithmic bias and the emergence of intelligence. 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.