Originally from Argentina, I got my MS in Astronomy from La Plata National University in 2005 and my PhD in Physics from University of Buenos Aires in 2010. During my graduate studies I focused on gravitational field theories and various extensions of General Relativity. Afterwards, I continued my work on theoretical physics and cosmology at Brandeis University. I joined Harvard-Smithsonian Center for Astrophysics in 2013 to work computational stellar astrophysics. In 2018, I became part of the Institute for Applied Computational Science at Harvard University, where I used machine learning algorithms to conduct data driven astrophysical research. Currently, I am an astrophysicist at Harvard-Smithsonian Center for Astrophysics, working for NASA's Chandra X-ray Observatory.   

I am interested in stellar evolution, activity, X-ray emission, and rotation, as well as the impact that stars have on their orbiting planets: star-planet interaction. I use magnetohydrodynamic simulations and deep learning to study problems like the spin-down evolution of young, active stars, high-energy radiation effect on exoplanetary environments and their habitability, and the orbital evolution of cataclysmic variables. More recently I have been focusing on uncertainty quantification in deep neural networks. In particular, developing probabilistic models and bayesian neural networks to retrieve fundamental stellar properties from observable quantities.