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.
Past Events
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.
KI Seminars (online)
Our introduction of the notion of a non-linear Kantorovich operator was motivated by the celebrated duality in the mass transport …
Modern machine learning methods, in particular deep learning approaches, have enjoyed unparalleled success in a variety of challenging …
Recent Posts
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
Benjamin Bloem-Reddy
Department of Statistics, University of British Columbia
Statistics, Machine Learning, Modeling, Inference, Computation, Probability
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
Maryam Fazel
Department of Electrical and Computer Engineering, University of Washington
Optimization Theory and Algorithms, Data Science and Machine Learning, Control Theory
Nassif Ghoussoub
Department of Mathematics, University of British Columbia
Partial Differential Equations
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
Bamdad Hosseini
Department of Applied Mathematics, University of Washington
Probability, Statistics, Applied Mathematics, Data Science, Uncertainty Quantification
Jingwei Hu
Department of Applied Mathematics, University of Washington
Kinetic Theory, Multiscale Modeling, Numerical Analysis, Partial Differential Equations, Scientific Computing
Young-Heon Kim
Department of Mathematics, University of British Columbia
Optimal Transporation, Partial Differential Equations, Calculus of Variations, Geometry
Philip Loewen
Department of Mathematics, University of British Columbia
Mathematical optimization, Calculus of Variations, Optimal Control, Optimization, Machine Learning
Soumik Pal
Department of Mathematics, University of Washington
Optimal Transporation, Probability Theory
Brendan Pass
Department of Mathematical and Statistical Sciences, University of Alberta
Optimal Transporation, Mathematical Economics, Mathematical Physics
Maurice Queyranne
Sauder School of Business, University of British Columbia
Combinatorial Optimization, Production Planning and Scheduling, Inventory Management
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
Dave Schneider
School of Environment and Sustainability, University of Saskatchewan
Global Institute for Food Security
Biological sequence analysis, Systems Biology, Functional Genomics, Comparative Genomics
Lior Silberman
Department of Mathematics, University of British Columbia
Number Theory (automorphic forms), Topology, Group theory, Metric geometry.
Stefan Steinerberger
Department of Mathematics, University of Washington
Analysis, PDEs, Spectral Theory, Harmonic Analysis
Danica J. Sutherland
Department of Computer Science, University of British Columbia
Learning and testing on sets and distributions, Learning “deep kernels”, Statistical Theory
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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