Conference: Geometry and Data Analysis

June 8-10, 2015

The University of Chicago


This is a workshop on geometric and topological approaches to statistical inference.  One of the main bottlenecks in the analysis of "big data" is caused by the poor scaling of fitting statistical distributions to high-dimensional data, especially when the data has an intricate correlation structure.  In recent years much work has been done using techniques from differential geometry, geometric analysis, algebraic geometry, and algebraic topology to produce algorithms and gain insight in some circumstances.

Conference Schedule

Invited Speakers

Yuliy Baryshnikov  |  University of Illinois at Urbana-Champaign

Reading Doodles and Scribbles

Mikhail Belkin  |  The Ohio State University

Learning a Hidden Basis from Imperfect Measurements

Omer Bobrowski  |  Duke University

Maximal cycles in random geometric complexes

Dima Burago  |  Pennsylvania State University

On discretization and approximation in Metric geometry and PDEs

Gunnar Carlsson  |  Stanford University

The Shape of Data

Frederic Chazal  |  INRIA

Subsampling methods for persistent homology

Rob Ghrist  |  University of Pennsylvania

Algebraic-Topological Data Structures

Ezra Miller  |  Duke University

Persistent interactions between biology, topology, and statistics

Washington Mio  |  Florida State University

The Shape of Data and Probability Measures

Sayan Murkhejee  |  Duke University

Geometry of Mixture Models

Hal Schenck  |  University of Illinois at Urbana-Champaign

Trading networks and Hodge theory

Katharine Turner  |  University of Chicago

Reconstruction of compact sets using cone fields

Michael Woods  |  Ayasdi

The Shape of Data


Registration information

Registration has closed.


Hotel information


as of June 6, 2015


The Stevanovich Center is supported by the generous philanthropy of University of Chicago Trustee Steve G. Stevanovich, AB '85, MBA '90.