April 3-9, 2016 Organizers: Krzysztof Oleskiewicz, University of Warsaw Elchanan Mossel, University of Pennsylvania Ryan O’Donnell, Carnegie Mellon University Related Links: Discrete Analysis: Beyond the Boolean Cube (2014) Analysis of Boolean Functions: New Directions and Applications (2012) Analysis of Boolean Functions Blog This third symposium for Analysis of Boolean Functions focused on "New Analytic...
April 10-16, 2016 Organizers: Werner Mueller, Mathematisches Institut der Universität Bonn Sug Woo Shin, UC Berkeley Nicolas Templier, Cornell University Related Links: Geometric Aspects of the Trace Formula (external site) 2014 Simons Symposium on Families of Automorphic Forms and the Trace Formula The second gathering of the Simons Symposium on the Trace Formula paved...
In this talk, Robert Kirshner will show how we discovered cosmic acceleration and present evidence that we live in a universe that is only 4 percent ordinary matter, with the balance being dark matter and dark energy.
April 17-23, 2016 Organizers: Fedor Bogomolov, Courant Institute of Mathematical Sciences Brendan Hassett, Brown University Yuri Tschinkel, Simons Foundation Related Links: 2012 Simons Symposium on Geometry Over Nonclosed Fields 2015 Simons Symposium on Geometry Over Nonclosed Fields The focus of this third symposium on Geometry Over Nonclosed Fields was zero-cycles and related Chow-theoretic and...
In this lecture, Alexei Borodin will illustrate how these two concepts work together in examples from random matrices to random interface growth.
Christopher Walsh will review recent work on ‘somatic mutations’ — de novo mutations that are present in some brain cells but not in all cells of the body — in several neurological conditions associated with intellectual disability and seizures.
In this lecture, Thomas Schulthess will show how recent developments in architecture have moved us away from traditional abstractions, forcing software development and mathematical algorithms to acknowledge the physical reality of computing systems.
A canonical task in machine learning is to fit a model to a dataset. Sanjeev Arora will describe models fitted to real-life datasets, which display randomlike properties that can offer insights into the algorithms used for the task.
In this lecture, Eric Betzig will describe advanced optical tools being developed to help scientists delve deeper into the complexity of biological systems.
