New KI Event: The Kantorovich Initiative will be co-sponsoring a Summer School on Optimal Transport, Stochastic Analysis and Applications to Machine Learning at KAIST in June 2024.

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. To know more about optimal transport, check out the wiki created by students at UC Santa Barbara and maintained by Katy Craig. Contributions are welcome! https://otwiki.xyz

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

Past Events

(CANCELLED) CAMS-PIMS Symposium on Optimal Transport and Applications
Unfortunately this event has been cancelled. When it is rescheduled, it will be re-added to this website.

The Center for Advanced Mathematics and the Pacific Institute for the Mathematical Sciences are organizing a Symposium on Optimal Transport and Applications at the American University of Beirut from November 6-11, 2023. Registration is now open. The event will include minicourses on the following topics

  • Introductory course on Optimal Transport (Brendan Pass, University of Alberta)
  • Numerical Methods in Optimal Transport (Quentin Mérigot, Paris-Saclay University)
  • Stochastic Optimal Transport and Finance (Walter Schachermayer, University of Vienna)
  • Optimal Transport in Physics and Cosmology (Yann Brenier, CNRS)

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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Jiajin Li

Sauder School of Business, University of British Columbia

Mathematical Optimization, Machine Learning, (Distributionally) Robust Optimization

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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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Dan Mikulincer

Department of Mathematics, University of Washington

Probability, High Dimensional Geometry, Optimal Transport, Mathematics of Data Science

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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

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