CAMS-PIMS Symposium on Optimal Transport and Applications

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CAMS-PIMS Symposium on Optimal Transport and Applications is postponed to a later date

American University of Beirut

Organized by the Center for Advanced Mathematical Sciences and the Pacific Institute for the Mathematical Sciences , the goal of this symposium is to introduce graduate and senior undergraduate students to the basic theory, but also to many of its applications and to recent breakthroughs in the field. This event, which is supported by the Kantorovich Initiative, will provide an environment which will facilitate interactions between experts, identify new challenges, and help jumpstart collaborative work.

Optimal Transport theory emerged more than two centuries ago as an engineering problem posed by Gaspard Monge just before the French revolution. Its rich mathematical structure was first revealed by the Russian Nobel-prize winner Kantorovich during world War II, and then about 30 years ago by Yann Brenier in his work on fluid dynamics, Robert McCann in his forays in mathematical physics, and by Wilfrid Gangbo and Craig Evans who, using PDE methods, eventually solved the original problem of Monge. Many other breakthroughs followed, led by Luigi Ambrosio, Felix Otto and their schools leading to the recent Fields medals for Cedric Villani and Alessio Figalli.

The richness of the theory of optimal transport stems from its central role in many branches of mathematics, be they theoretical, applied or computational. Indeed, the basic problem of transporting a probability measure onto another probability measure, while minimizing a given cost of the transport, is now at the core of a wide range of problems in mathematics, physics, economics, statistics, computer science, biology and neuroscience. Recent theoretical and computational advances have paved the way for major breakthroughs in all these areas.

The theory of optimal mass transport has had an impact on various classical branches of mathematics: geometry, analysis, dynamics, partial differential equations, and fluid mechanics. Since it defines a distance between very general distributions and entities of various nature, essential for object recognition and classification, it is now widely investigated in signal processing, machine learning, weather prediction, neuroscience, computer vision, and astrophysics.

Mini-courses

Introductory course on Optimal Transport, Brendan Pass (Department of Mathematical and Statistical Sciences, University of Alberta)
Numerical methods in Optimal Transport, Quentin Mérigot (Mathematical Institute, Paris-Saclay University)
Stochastic Optimal Transport and Finance, Walter Schachermayer (Faculty of Mathematics, University of Vienna)
Optimal Transport in Physics and Cosmology, Yann Brenier (CNRS, France)

Scientific Committee

Yann Brenier (CNRS, France)
Guillaume Carlier (Centre de Recherche en Mathématiques de la Décision, Université Paris Dauphine, France)
Wilfrid Gangbo (Department of Mathematics, The University of California, Los Angeles, USA)
b (Department of Mathematics, the University of British Columbia, Canada)
Robert McCann (Department of Mathematics, University of Toronto, Canada)
Soumik Pal (Department of Mathematics, University of Washington, USA)

Local Committee

Victor Araman (the Olayan School of Business, American University of Beirut)
Ayman kachmar (Department of Mathematics, Lebanese University)
Ahmad Sabra (Department of Mathematics, American University of Beirut)
Jihad Touma (Department of Physics, CAMS, American University of Beirut)

For more information on this event please email Hiba Hammoud

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American University of Beirut

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