Departmental Colloquium 2017-2018

Thursdays, 4:00 PM, JWB 335



Fall 2017

August 31: AWM Colloquium
Speaker: Moon Duchin, Tufts University
Title: Can you hear the shape of a billiard table?
Abstract: There are many ways to associate a spectrum of numbers to a surface: two of the most classically studied are the eigenvalues of the Laplacian and the lengths of closed geodesics. People often ask whether two different surfaces can have the same spectrum of numbers, and there's a long and beautiful story attached to that question. Here's a twist on the setup: now consider a polygon in the plane, and label its sides with letters. Follow a billiard ball trajectory around the surface and record the "bounce sequence," or the sequence of labels hit by the ball as it moves. Is it possible for two different billiard tables to have all the same bounces?

Special Colloquium: Tuesday September 26, 4-5pm, JWB 335:
Speaker: David Higdon, Virginia Tech
Title: A small, biased sample of experiences involving statistical modeling and big data
Abstract: Statistical modeling is the art of combining mathematical/probabilistic models and data to infer about some real-life system. The structure, volume and diversity of modern data sources brings out a number of computational challenges in applying statistical modeling to such data. This talk will cover three different examples that grapple with big data and computational issues in statistical inference: computer model calibration for cosmological inference; response surface/regression modeling in big data settings; combining varieties of automatically collected data to better manage a supply chain of a large industrial corporation. A bit more technical detail will be given for the first example in cosmology where observations are combined with computational model runs carried out at different levels of resolution to infer about parameters in the standard model. The other two applications will be discussed from a broader perspective, motivating thoughts regarding commonalities and differences in these different strategies for big data analytics.

November 2:
Speaker: Sarang Joshi, University of Utah
Title: Riemannian Brownian Bridges and Metric Estimation on Landmark Manifolds
Abstract: We present an inference algorithm and connected Monte Carlo based estimation procedures for metric estimation from landmark configurations distributed according to the transition distribution of a Riemannian Brownian motion arising from the Large Deformation Diffeomorphic Metric Mapping (LDDMM) metric. The distribution possesses properties similar to the regular Euclidean normal distribution but its transition density is governed by a high-dimensional non-linear PDE with no closed-form solution. We show how the density can be numerically approximated by Monte Carlo sampling of conditioned Brownian bridges, and we use this to estimate parameters of the LDDMM kernel and thus the metric structure by maximum likelihood. (Joint with Stefan Sommer, Alexis Arnaudon, Line Kuhnel)

November 9:
Speaker: Benedek Valko, University of Wisconsin
Title: TBA
Abstract: TBA

November 16 3:00-4:00pm: (Note special time)
Speaker: Claudia Polini, University of Notre Dame
Title: TBA
Abstract: TBA

November 30: Math/CSME Colloquium
Speaker: Natasha Speer, The University of Maine
Title: TBA
Abstract: TBA

December 7:
Speaker: Tim Austin, UCLA
Title: TBA
Abstract: TBA

Spring 2018

April 12:
Speaker: David Ayala, Montana State University
Title: TBA
Abstract: TBA

April 24 (Tuesday):
Speaker: Donna Testerman, EPFL
Title: TBA
Abstract: TBA