Organizers:

• Marina Meila, University of Washington

• Facundo Mémoli, The Ohio State University

• Jose Perea, Northeastern University

• Nicolás García Trillos, University of Wisconsin-Madison

• Soledad Villar, Johns Hopkins University

Volunteer Assistants:

• Luis Scoccola, Northeastern University

• Chenghui Li, University of Wisconsin-Madison

• Qingsong Wang, The Ohio State University

The goal of this workshop is to bring together PhD students and early career researchers in Mathematics, Statistics, and related fields, with the aim of contributing to the further development of mathematical foundations for data science. Specifically, our aim is to create a collective platform for the development of a strong theoretical understanding of the interaction between modern problems in data science (such as the representation of complex spatio-temporal dynamics using topological and geometric summaries, adversarial learning and its connections to regularization and generalization, and the design of novel objective functions for robust data analysis) and areas in pure and applied mathematics such as spectral geometry, metric measure space geometry, optimal transportation, and topological data analysis. Each of these areas has been steadily gaining maturity and prominence in applied domains including neuroscience, machine learning, sociology, and materials science.

This workshop is sponsored by the American Mathematical Society through NSF grants (http://www.ams.org/programs/research-communities/mrc).