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.
The common theme of this summer school is the mathematics of Monge-Kantorovich optimal transport (OT). The stunning mathematical development of OT has recently permeated into several fields of applications. Our speakers, chosen from the fields of analysis, biology, data science, economics, and probability, are leaders in their respective fields whose work intimately involves OT. Our goal is to expose talented students and junior researchers to the exciting and manifold research opportunities arising from OT and its applications, through attending lectures and interacting with the speakers as well as their peer participants. We strongly encourage participation by a diverse audience and welcome attendees from traditionally underrepresented socio-economic and cultural groups.
As the school is planned at the beginning of the summer season in Seattle, the participants can also enjoy the natural beauty of the Pacific Northwest and feel the energy of one of leading tech metropolises in the United States and around the world.
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
Econometrics, Nonparametric Statistics
Optimization Theory and Algorithms, Data Science and Machine Learning, Control Theory
Partial Differential Equations
Robust Statistical Machine Learning, Learning Feature Representations of Complex Data, Computationally-Efficient Optimization Algorithms for Learning and Inference
Probability, Statistics, Applied Mathematics, Data Science, Uncertainty Quantification
Kinetic Theory, Multiscale Modeling, Numerical Analysis, Partial Differential Equations, Scientific Computing
Optimal Transporation, Partial Differential Equations, Calculus of Variations, Geometry
Mathematical optimization, Calculus of Variations, Optimal Control, Optimization, Machine Learning
Optimal Transporation, Probability Theory
Optimal Transporation, Mathematical Economics, Mathematical Physics
Optimal transport, Imaging Sciences, Machine Learning
Combinatorial Optimization, Production Planning and Scheduling, Inventory Management
Interplay between Theory and Experiment in Natural Science, Time-courses of high dimensional gene expression data, Probability, Statistics, Optimization
Biological sequence analysis, Systems Biology, Functional Genomics, Comparative Genomics
Number Theory (automorphic forms), Topology, Group theory, Metric geometry.
Analysis, PDEs, Spectral Theory, Harmonic Analysis
Learning and testing on sets and distributions, Learning “deep kernels”, Statistical Theory
Deep generative modeling, Amortized Inference, Probabilistic Programming, Reinforcement Learning, Applied Probabilistic Machine Learning
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