“Optimal Transport, Topological Data Analysis and Applications to Shape and Machine Learning”
Description: The last few years have seen the rapid development of mathematical methods for the analysis of shape data arising in biology and computer vision applications. Recently developed tools coming from the fields of optimal transport and topological data analysis have proved to be particularly successful for these tasks. The goal of this conference is to bring together researchers from these communities to share ideas and to foster collaboration between them. Talks will focus on computational and theoretical aspects as well as on applications, with a focus on shape analysis and machine learning.
Due to uncertainties about safety and travel restrictions, the workshop will be converted to a virtual format. It will still take place during the scheduled week (the week of July 27).
Dates: July 27th to July 31st 2020
Venue: Online via MBI at OSU
The MBI page contains more detailed information about the schedule and will contain titles and abstracts.
This event is open to the public, but requires registration to attend. The registration page is here.
Organizers: Nicolas Garcia Trillos (UW), Facundo Memoli (OSU), Tom Needham (FSU), and Jose Perea (MSU)
Henry Adams (Colorado State University)
Claire Brécheteau (Université de Nantes)
Chao Chen (Stony Brook)
Edward Chien (MIT)
Samir Chowdhury (Stanford)
Jessi Cisewski-Kehe (Yale)
Katy Craig (UCSB)
Justin Curry (University at Albany SUNY)
Xianfeng David Gu (Stony Brook)
Julie Delon (Université Paris Descartes)
Brittany Fasy (Montana State University)
Varun Jog (University of Wisconsin)
Marcel Klatt (Göttingen)
Théo Lacombe (Inria Saclay)
Robert McCann (University of Toronto)
Guido Montufar (UCLA)
Sayan Mukherjee (Duke)
Sinho Chewi (MIT)
Radmila Sazdanovic (North Carolina State University)
Justin Solomon (MIT)
Pavan Turaga (Arizona State University)
Bei Wang (University of Utah)
Christoph Weitkamp (Göttingen)
Hongteng Xu (Infinia ML/Duke)
Lori Ziegelmeier (Macalester College)