Bo'az Klartag (Weizmann Institute of Science; chair), James Lee (University of Washington), Prasad Tetali (Georgia Institute of Technology), Jelani Nelson (Harvard University), Ramon Van Handel (Princeton University)
Because of COVID-19, we cannot schedule in-person events on the Berkeley campus through December 2020. This workshop will take place online. It will be open to the public for online participation. Please register to receive the zoom webinar access details.
Isoperimetric and concentration inequalities are a cornerstone of many results spanning numerous areas of theoretical computer science: randomized algorithms and Monte Carlo sampling methods, hardness of approximation, and learning theory, to name a few. Indeed, while concentration of measure is a classical subject that has long been studied somewhat independently of applications, some of the most remarkable results in the field have been inspired by the potential for applications in computer science. Some of the central conjectures open today are also motivated by computational applications, notably the Kannan–Lovász–Simonovits conjecture. Concentration phenomena arise in various settings, often studied by separate communities: over gaussian space, Riemannian manifolds, discrete product spaces, and algebraic structures. In the past, seemingly unrelated ideas and techniques have been successfully adapted between the different settings. In spite of this, collaboration among the various communities interested in concentration of measure remains limited. The goal of the workshop is to bring high-dimensional geometers and probabilists together with computer scientists, to share ideas on applications as well as state-of-the-art techniques.
Further details about this workshop will be posted in due course. Enquiries may be sent to the organizers workshop-hd2 [at] lists.simons.berkeley.edu (at this address).