Matias Delgadino

Delgadino's research interests are mostly related to mathematical modeling through partial differential equations (PDEs). For the most part, his work is devoted to understanding self-organization phenomena in systems with a large number of particles/agents. Using this perspective, he is currently trying to understand the dynamics of parameter training in commonly used machine learning algorithms. He did his undergraduate studies at the Universidad Nacional de Córdoba (Argentina), and then went to the University of Maryland for his Ph.D. in Applied Mathematics under the supervision of Antoine Mellet. After his Ph.D., he held postdoctoral positions at UNESCO's ICTP (Italy) under the supervision of Francesco Maggi and Imperial College (United Kingdom) under the supervision of Greg Pavliotis and Jose Carrillo.

Delgadino's research focuses on mathematical modeling through partial differential equations, particularly in understanding self-organization phenomena in systems with a large number of particles or agents. Currently, he is exploring the dynamics of parameter training in commonly used machine learning algorithms from this perspective.