This workshop focuses on extracting structure from high-dimensional datasets. In particular, it will address how to reliably uncover the laws that govern the dynamics being investigated and how to discover and describe the geometry present in sets of data. The workshop will bring together researchers from a variety of fields, including statistical machine learning, applied analysis, dynamical systems, probability and stochastic processes, and computational mathematics for exchange of ideas.
- Antonin Chambolle École Polytechnique, Paris
- Frédéric Chazal INRIA Saclay
- Jerome Darbon Brown University
- Massimo Fornasier Technical University of Munich
- Yannis Kevrekidis Princeton University
- Nathan Kutz University Washington
- Gilad Lerman University of Minnesota
- Jianfeng Lu Duke University
- Facundo Memoli Ohio State University
- Sebastien Motsch Arizona State University
- Christof Schütte Freie Universität Berlin
- Andrew Stuart Caltech
- Eric Vanden-Eijnden Courant Institute, NYU
- Rachel Ward University of Texas, Austin
- Larry Wasserman Carnegie Mellon University
A limited amount of funds is available to support researchers in the early stages of their career who want to attend the program, especially for graduate students and post-doctoral fellows.
Deadline for applications for support is January 31.
Nicolás García-Trillos Brown University
Mauro Maggioni Johns Hopkins University
Hayden Schaeffer Carnegie Mellon University
Dejan Slepčev Carnegie Mellon University
Matthew Thorpe Carnegie Mellon University
Telephone: (412) 268-2545
Fax: (412) 268-6380
Funding provided by the National Science Foundation through the KI-net Grant and the Center for Nonlinear Analysis, CMU.