Matrices, Optimization, and Randomness with Applications in Data Science
23-MATH-09 MORA-DataS
PROJECT MISSION
The research project "Matrices, Optimization, and Randomness with Applications in Data Science (MORA-DataS)" is composed of research teams from Bolivia, Chile, France, and Peru. This project is funded by the regional program MATH-Amsud in cooperation with UMSA (Bolivia), ANID, CMM (Chile), MEAE (France), and CONCYTEC (Peru).
The aim of this project is to study diverse optimization models, deterministic and stochastic, and to investigate various problems in matrix analysis with potential applications in data science. Some of the research topics are computing angles between convex cones, inverse eigenvalue problems, proximal algorithms for symmetric cone optimization, nonlinear second-order cone programming problems, nonsmooth joint chance constrained optimization problems, and Euclidean Jordan algebras for optimization.
SCIENTIFIC COORDINATORS
David Sossa, Universidad de O'Higgins, Chile (International coordinator)
Susana Arela, Universidad de Tarapacá, Chile
Hans Nina, Universidad de Antofagasta, Chile
Julio López, Universidad Diego Portales, Chile
Charlie Lozano, Universidad Mayor de San Andrés, Bolivia
Erik Papa, Universidad Mayor de San Marcos, Peru
Valentina Sessa, École Nationale Superieure de Mines de Paris, France
Wim van Ackooij, EDF Paris-Saclay, France