Pacific Northwest Probability Seminar

The Nineteenth Northwest Probability Seminar
November 4, 2017
Supported by the Pacific Institute for the Mathematical Sciences (PIMS) and Microsoft Research.

 
 
The Birnbaum Lecture in Probability will be delivered by Michel Ledoux (University of Toulouse) in 2017.

The 19th Northwest Probability Seminar, a one-day mini-conference organized by the University of Washington, the Oregon State University, the University of British Columbia, the University of Oregon, and Microsoft Research, will be held on November 4, 2017. The conference will be hosted at Microsoft, supported by Microsoft Research and the Pacific Institute for the Mathematical Sciences (PIMS).

There is no registration fee. Breakfast, lunch, and coffee will be free.

The talks will take place in Building 99 at Microsoft. Parking at Microsoft is free.

Schedule

10:00 — 11:00 Coffee and muffins
11:00 — 11:40 TBA ()
TBA
11:50 — 12:30 TBA ()
TBA
12:30 — 1:40 Lunch (catered)
1:10 — 2:15 Probability demos and open problems (overlaps with lunch)
2:20 — 3:10 Michel Ledoux (University of Toulouse; Birnbaum Lecture)
TBA
3:20 — 4:00 TBA ()
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4:00 — 4:30 Tea and snacks
4:30 — 5:10 TBA ()
TBA
6:00 — no host dinner at Haiku sushi & seafood buffet, downtown Redmond

Titles and abstracts

Persi Diaconis (Stanford University)

Title: An Analysis of Spatial Mixing

Abstract: In joint work with Soumik Pal, we study natural mixing processes where cards (or dominoes or mahjong tiles) are 'smushed' around on a table with two hands. How long should mixing continue. If things are not well mixed, what patterns remain? We study this in practice (!): experiments indicate that about 30 seconds of smushing suffice to mix 52 cards. We also study it in theory introducing a variety of models which permit analysis. Part of the analysis passes to a reflecting, jump- diffusion limit and uses this and a novel 'shadow coupling' to give reasonably precise bounds on the mixing time.

Michel Ledoux (University of Toulouse)

Title: Optimal matching of Gaussian samples

Abstract: Optimal matching problems are random variational problems widely investigated in the mathematics and physics literature. We discuss here the optimal matching problem of an empirical measure on a sample of iid random variables to the common law in Kantorovich-Wasserstein distances, which is a classical topic in probability and statistics. Two-dimensional matching of uniform samples gave rise to deep results investigated from various view points (combinatorial, generic chaining). We study here the case of Gaussian samples, first in dimension one on the basis of explicit representations of Kantorovich metrics and a sharp analysis of more general log-concave distributions in terms of their isoperimetric profile (joint work with S. Bobkov), and then in dimension two (and higher) following the PDE and transportation approach recently put forward by L. Ambrosia, F. Stra and D. Trevisan.

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Dinner info

No host dinner (6:00 onwards) at Haiku sushi & seafood buffet, downtown Redmond