[Ml-stat-talks] Fwd: Princeton Optimization Seminar + Wilks Statistics Seminar: Caroline Uhler, MIT - Today, Dec. 3rd, 4:30 PM

Barbara Engelhardt bee at princeton.edu
Thu Dec 3 12:40:34 EST 2015

Talk of interest today.

---------- Forwarded message ----------
From: Carol Smith <carols at princeton.edu>
Date: Thu, Dec 3, 2015 at 9:16 AM
Subject: Princeton Optimization Seminar + Wilks Statistics Seminar:
Caroline Uhler, MIT - Today, Dec. 3rd, 4:30 PM
To: wilks-seminar at princeton.edu

*-----   **Princeton Optimization Seminar + Wilks Statistics Seminar**

DATE: Today, December 3, 2015

TIME:  4:30 PM

LOCATION:  Sherrerd Hall 101

SPEAKER: Caroline Uhler, MIT

TITLE: Parameter Estimation for Linear Gaussian Covariance Models

Linear Gaussian covariance models are Gaussian models with linear
constraints on the covariance matrix. Such models arise in many
applications, such as stochastic processes from repeated time series data,
Brownian motion tree models used for phylogenetic analyses, and network
tomography models used for analyzing connections in the Internet. Maximum
likelihood estimation in this class of models leads to a non-convex
optimization problem that typically has many local maxima. Using recent
results on the asymptotic distribution of the extreme eigenvalues of the
Wishart distribution, we prove that maximum likelihood estimation for
linear Gaussian covariance models is in fact, with high probability,
concave in nature and therefore can be solved using iterative hill-climbing
Caroline Uhler is an assistant professor in EECS and IDSS at MIT. She holds
an MSc in Mathematics, a BSc in Biology, and an MEd in High School
Mathematics Education from the University of Zurich. She obtained her PhD
in Statistics from UC Berkeley in 2011. After short postdoctoral positions
at the Institute for Mathematics and its Applications at the University of
Minnesota and at ETH Zurich, she joined IST Austria as an assistant
professor (2012-2015). Her research focuses on mathematical statistics, in
particular on graphical models and the use of algebraic and geometric
methods in statistics, and on applications to biology. She is an elected
member of the International Statistical Institute and she received a Sofja
Kovalevskaja Award from the Humboldt Foundation and a START Award from the
Austrian Science Fund.
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