OT + X

The Kantorovich Initiative is offering regular online courses on Optimal Transport + ‘X’, where, in different iterations, ‘X’ is chosen from the many disciplines in which optimal transport (OT) plays an important role, including economics and finance, data science/statistics, computation, biology, etc. These courses will have two main objectives: first, to introduce a wide range of students to the exciting and broadly applicable research area of optimal transport, and second, to explore more closely its applications in a particular field, which will vary from year to year (represented by ‘X’ in the title).

In Fall 2020 OT+Economics was taught by Brendan Pass from University of Alberta. The course also featured guest lectures by Yanqin Fan (UW Econ), Soumik Pal (UW Math) and Guillaume Rabault from HSBC Global Asset Management.

The first part of the course surveyed the basic theory of optimal transport. The second part of the course develops applications in economics, such as matching problems, estimation of incomplete information, multivariate generalizations of quantiles, industrial organization, contract theory, and financial engineering.

In Fall 2021 OT+ML was taught by Zaid Harchaoui and Soumik Pal from the University of Washington, Seattle.

The first part of the course surveyed the basic theory of optimal transport. The second part covered numerous aspects of OT in statistics and machine learning, including entropic regularization, Wasserstein barycenters, statistical estimation and rates of convergence.

In Fall 2022 we are scheduled to offer OT+biology-single cell analysis taught by Geoff Schiebinger from UBC. The course will cover applications of OT to developmental cell biology. If you are interested in enrolling for this course, please follow the instructions on this PIMS regisration webpage.