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Institute for Mathematical and Statistical Innovation (IMSI)

This long program will focus on the newest development of UQ methodologies and how they can improve AI systems and provide solutions to modeling complex systems. It will also give an outlook on future UQ directions and challenges. Through all the activities proposed, the program will bring together interested parties, researchers, practitioners, and students, from different areas of UQ, promote communication, and further break down the barriers between disciplines. The program also has a significant mentoring component, which connects researchers and students at different career stages.

Systèmes, contrôle et automatisation,    Mise en œuvre et applications de l'intelligence artificielle

American Institute of Mathematics, Pasadena, California (AIM)

This workshop, sponsored by AIM and the NSF, will be devoted to providing formal analysis and theoretical justification of Markov chain methods for sampling graph partitions. While these methods have seen significant use in empirical projects motivated by political redistricting, there are still many open questions about standard properties of the Markov chain proposals. This includes determining sets of parameters for the random walks and underlying graphs under which we can provide rigorous guarantees about irreducibility, mixing time, and more. Additionally, recent work has further shown the importance of understanding the properties of random spanning trees and tree-weighted partitions used in these methods. The goal of this workshop is to bring together experts in Schramm–Loewner evolution and related processes (including loop-erased random walks), Markov chain theory, spanning tree methods, computational geometry, and graph theory (planar/near-planar graphs and their random substructure) to address these fundamental problems.

Simons Laufer Mathematical Sciences Institute (SLMath)

This summer school offers an exceptional opportunity for participants to delve into the intricate realm of statistical optimal transport theory. This captivating field stands at the crossroads of multiple disciplines, drawing from a rich tapestry of mathematical insights from diverse subjects, including partial differential equations, stochastic analysis, convex geometry, statistics, and machine learning, crafting a vibrant and interdisciplinary landscape. The foremost objective of this summer school is to create a dynamic learning environment that unites students from diverse backgrounds such as PDE theory, probability, or optimal transport. Throughout the program, participants will embark on a journey that will not only enhance their comprehension of the distinct elements of statistical optimal transport but will also foster the integration of diverse disciplines. This integration will enable them to tackle the forefront challenges in statistics and machine learning where optimal transport stands as a potent and indispensable tool. By the end of the program, participants will leave with not only a comprehensive skill set but also a profound understanding of the synergy between optimal transport and the realm of statistics and machine learning, ready to apply their knowledge to the forefront of this exciting field.

Cours et évènements pour étudiants en mathématiques,    Optimisation, recherche opérationnelle

American Statistical Association

American Statistical Association (ASA)