Introduction to Optimal Transport: Applications to Machine Learning, Cognitive Science, and Computational Biology

Optimal transport is a simple yet powerful mathematical framework for comparing and connecting probability distributions and/or data sets. It is rapidly gaining popularity across a range of fields. This workshop introduces the fundamental concepts in an accessible way and showcases a variety of applications, including machine learning, cognitive science, and computational biology.

The format of this workshop is presentation with open Q&A. Python notebooks will be made available after the session.

Participants should have a basic familiarity with probability; matrix/vector operations; logarithms/exponents. No coding experience required.

Lunch will be provided to all participants. An email will be sent regarding dietary restrictions.

Meet the Facilitators

Sarah-Jane Leslie, Professor of Philosophy and the Center for Statistics and Machine Learning, and Co-Director of Natural and Artificial Minds
Julian Gold, DataX Data Scientist, Center for Statistics and Machine Learning

Sponsorship

This workshop is co-sponsored by Natural and Artificial Minds, the Center for Statistics and Machine Learning, and the Princeton Institute for Computational Science and Engineering.

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Co-hosted with: Research Computing

Contact the organizers