The Gaussian distribution describes fluctuations arising in many systems across mathematics, science and society. However, complex random systems such as those related to interface growth, big data, stochastic optimization, traffic / queuing flow, and stochastic PDEs often do not fall into this universality class. This talk will explain how these and other important types of real world systems fall into a different universality class -- the so called Kardar-Parisi-Zhang class. This talk will be aimed at a very general audience and will feature almost no equations and lots of interesting phenomena, videos and examples.
About the speaker:
Ivan Corwin is currently a Professor of Mathematics at Columbia University. His thesis included (in joint work with Amir and Quastel) the exact solution to the Kardar-Parisi-Zhang stochastic PDE. Subsequently, with Borodin, he introduced and developed the theory of Macdonald processes. Along with other collaborators, he has developed the area of Integrable Probability, including the study of stochastic vertex models and the Markov duality approach. He has also worked on discrete approximation theory to stochastic PDEs.
Corwin received his Ph.D. from the Courant Institute in 2011 and has since held positions at Microsoft Research, MIT, Institute Henri Poincare, and now Columbia. He was a Clay Research Fellow and is presently a Packard Fellow and a Fellow of the Institute of Mathematical Statistics. He was the recipient of the Alexanderson Award, Rollo Davidson Prize, Young Scientist Prize of the IUPAP, and gave an invited lecture at the 2014 ICM.
Prof Corwin's talk is supported by The Mahler Lectureship, organised by AustMS and AMSI. The Mahler Lectureship is awarded every two years to a distinguished mathematician who preferably works in an area of mathematics associated with the work of Professor Mahler.