Matrices, Optimization, and Randomness with Applications in Data Science 

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