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, pose great challenges to computer scientists. 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 the slime mold dynamics and iteratively reweighted least squares method, a sampling algorithm inspired from Hamiltonian dynamics, and algorithms to control bias in data summarization.
Nisheeth Vishnoi is a faculty member of the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne. 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. His research focuses on foundational problems in algorithms, optimization, statistics, and complexity, and how tools from these areas can be used to address emerging algorithmic questions in society, nature, and machine learning. Topics from these areas that he is currently interested in include algorithmic bias, understanding emergent behavior in natural systems, and developing algorithms that can go beyond the worst-case in machine learning. 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.