Concentration of Measure Phenomena

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.

This workshop will now be held virtually. The workshop is being live streamed on our website. Full participation (including the capacity to ask questions) will be available via Zoom webinar. A link to the Zoom webinar will be shared with registrants closer to the event date. Please register by clicking the registration link above.

If you require accommodation for communication, please contact our Access Coordinator at simonsevents [at] berkeley.edu with as much advance notice as possible.