The Kantorovich Initiative is dedicated towards research and dissemination of modern mathematics of optimal transport towards a wide audience of researchers, students, industry, policy makers and the general public.

The group was convened by Young-Heon Kim (University of British Columbia), Soumik Pal (University of Washington) and Brendan Pass (University of Alberta), with support from the Pacific Institute for the Mathematical Sciences.

Upcoming Events

Econometrics and Optimal Transport Workshop - June 2023
Please see the event website for more details or to register. Confirmed Speakers Adrien Blanchet (Toulouse School of Economics, Mathematics) Jose Blanchet (Stanford University, Management Science and Engineering) Xiaohong Chen (Yale University, Economics) Victor Chernozhukov (Massachusetts Institute of Technology, Economics) Tim Christensen (University College London, Economics) Nabarun Deb (University of British Columbia/University of Chicago-Booth, Statistics) Xavier d’Haultfoeuille (ENSAE, Economics) Rui Gao (University of Texas-Austin, Mathematics) Florian Gunsilius (University of Michigan, Economics) Tetsuya Kaji (University of Chicago-Booth, Econometrics/Statistics) Francis Kramarz (ENSAE, Economics) Lixiong Li (Johns Hopkins University, Economics) Jean-Michel Loubes (Universite de Toulouse, Mathematics) Marcel Nutz (Columbia University, Statistics) Guillaume Pouliot (University of Chicago, Economics) Bernard Salanie (Columbia University, Economics) Matt Shum (California Institute of Technology, Economics) Xiaoting Sun (Simon Fraser University, Economics) Ruodu Wang (University of Waterloo, Statistics) Organizing Committee Yanqin Fan (University of Washington, Department of Economics) Alfred Galichon (New York University, Department of Economics) Marc Henry (Pennsylvania State University, Department of Economics) Soumik Pal (University of Washington, Department of Mathematics) Brendan Pass (University of Alberta, Department of Mathematical and Statistical Sciences)

Past Events

Locally Lipschitz selection in the principal-agent problem
Robert McCann (The University of Toronto)
We prove the agent’s choice will be a locally Lipschitz function of their type in the subclass of principal-agent problems considered …
Locally Lipschitz selection in the principal-agent problem
IFML+KI retreat 2023

The second Kantorovich Initiative Retreat will take place on Thursday February 2nd, 2023 in Zillow Commons, 4th floor, Gates Center. This is in collaboration with UW Institute for Foundations in Machine Learning (IFML).

KI retreats are local one day conferences where KI members and their research groups get together to socialize and discuss potential collaborations.

IFML+KI retreat 2023

KI Seminars (online)

About Us

We are inspired by the works of mathematician and economist Leonid Kantorovich who is considered as one of the fathers of the modern theory of linear programming and of optimal mass transport. Kantorovich was interested in the economic aspects and application of his work, for which he won the Nobel prize in economics in 1975. The current activities of KI are being supported by grants from the Pacific Institute for the Mathematical Sciences and the National Science Foundation

Affiliated Faculty

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Benjamin Bloem-Reddy

Department of Statistics, University of British Columbia

Statistics, Machine Learning, Modeling, Inference, Computation, Probability

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Khanh Dao Duc

Department of Mathematics, University of British Columbia

Molecular and Cell biology, Gene expression, Cryo-EM microscopy, Biological shape and image analysis, Machine learning and Applied stochastic processes

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Yanqin Fan

Department of Economics, University of Washington

Econometrics, Nonparametric Statistics

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Maryam Fazel

Department of Electrical and Computer Engineering, University of Washington

Optimization Theory and Algorithms, Data Science and Machine Learning, Control Theory

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Nassif Ghoussoub

Department of Mathematics, University of British Columbia

Partial Differential Equations

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Zaid Harchaoui

University of Washington

Department of Statistics

Robust Statistical Machine Learning, Learning Feature Representations of Complex Data, Computationally-Efficient Optimization Algorithms for Learning and Inference

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Bamdad Hosseini

Department of Applied Mathematics, University of Washington

Probability, Statistics, Applied Mathematics, Data Science, Uncertainty Quantification

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Jingwei Hu

Department of Applied Mathematics, University of Washington

Kinetic Theory, Multiscale Modeling, Numerical Analysis, Partial Differential Equations, Scientific Computing

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Young-Heon Kim

Department of Mathematics, University of British Columbia

Optimal Transporation, Partial Differential Equations, Calculus of Variations, Geometry

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Philip Loewen

Department of Mathematics, University of British Columbia

Mathematical optimization, Calculus of Variations, Optimal Control, Optimization, Machine Learning

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Soumik Pal

Department of Mathematics, University of Washington

Optimal Transporation, Probability Theory

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Brendan Pass

Department of Mathematical and Statistical Sciences, University of Alberta

Optimal Transporation, Mathematical Economics, Mathematical Physics

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Gabriel Peyré

DMA, École Normale Supérieure.

Optimal transport, Imaging Sciences, Machine Learning

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Maurice Queyranne

Sauder School of Business, University of British Columbia

Combinatorial Optimization, Production Planning and Scheduling, Inventory Management

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Geoffrey Schiebinger

Department of Mathematics, University of British Columbia

Interplay between Theory and Experiment in Natural Science, Time-courses of high dimensional gene expression data, Probability, Statistics, Optimization

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Dave Schneider

School of Environment and Sustainability, University of Saskatchewan

Global Institute for Food Security

Biological sequence analysis, Systems Biology, Functional Genomics, Comparative Genomics

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Lior Silberman

Department of Mathematics, University of British Columbia

Number Theory (automorphic forms), Topology, Group theory, Metric geometry.

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Stefan Steinerberger

Department of Mathematics, University of Washington

Analysis, PDEs, Spectral Theory, Harmonic Analysis

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Danica J. Sutherland

Department of Computer Science, University of British Columbia

Learning and testing on sets and distributions, Learning “deep kernels”, Statistical Theory

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Amir Taghvaei

Department of Aeronautics & Astronautics

Nonlinear filtering/estimation, Reinforcement learning, Stochastic Thermodynamics, Optimal Transportation theory

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Frank Wood

Department of Computer Science, University of British Columbia

Deep generative modeling, Amortized Inference, Probabilistic Programming, Reinforcement Learning, Applied Probabilistic Machine Learning

Mailing List

We run the following mailing list to advertise group activities and related events. Please consider signing up to keep up to date with the latest developments.

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