# 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