Natali Soler Matubaro de Santi

Natalí Soler Matubaro de Santi develops statistical, computational, and machine learning methods for cosmology and astrophysics, with a focus on simulation-based inference, field-level analyses, and representation learning for large-scale structure. Her work combines modern machine learning techniques, such as normalizing flows, graph neural networks, and deep generative models, with physically motivated approaches to enable robust and scalable cosmological inference from increasingly complex datasets.

She is currently a Postdoctoral Scholar at the University of California, Berkeley, and an affiliated researcher at Lawrence Berkeley National Laboratory, where she works with Professor Uros Seljak.

Natalí received her Ph.D. in Physics from the University of São Paulo (USP) in 2024, under the supervision of Professor Raul Abramo. During her doctoral studies, she was a visiting researcher at the Flatiron Institute, Simons Foundation, in New York, where she worked with Dr. Francisco Villaescusa-Navarro and further developed machine learning approaches for astrophysical applications.

She holds a Master’s degree in Physics from the Federal University of São Carlos (UFSCar), completed in 2018 under the supervision of Professor Raphael Santarelli, and a Bachelor’s degree in Physics from the University of São Paulo (USP), Institute of Physics of São Carlos (IFSC), completed in 2015.