Dynamics and geometry from high dimensional data

Mar 14 - 16, 2017

Carnegie Mellon University
Mathematical Sciences

Visitor Guide

SCHEDULE and ABSTRACTS

CONFERENCE LECTURES

ABSTRACT

This workshop focuses on  extracting structure from high-dimensional data sets. 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.

GOALS

The aim of the workshop is to bring together researchers from a wide spectrum of disciplines studying geometry of data sets and data-driven ways to discover  and the rules that govern dynamic of physical, chamical or biological processes observed. The goal is to foster exchanges between applied scientists studying the phenomena and theoretical scientists who develop models and study their properties.

REGISTRATION REQUESTED

Invited participants can RSVP and provide the additional requested information about their stay, including the title and abstract of proposed talks, here.

Applications for participation can be made through the Online Application. Applicants who are also interested in making a poster presentation can specify their preference to do so in the “comments section” of their application. Due to space limitations, these requests are subject to approval by the organizers.

ORGANIZERS

CONFIRMED PARTICIPANTS

FUNDING

A limited amount of travel and local lodging is available for researchers in the early stages of their career who want to attend the full program, especially for graduate students and post-doctoral fellows.

INFORMATION FOR PARTICIPANTS

Visitor Guide

Email: slepcev@math.cmu.edu

CONFERENCE POSTER

ACKNOWLEDGMENT

Funding provided by the NSF through the KI-net Grant.