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 CfA in 2013 to work computational stellar astrophysics. Currently, I am part of the Institute for Applied Computational Science at Harvard University, where I use machine learning algorithms to conduct data driven astrophysical research.
I am interested in stellar evolution, activity, 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, 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.
Stellar Activity and Rotation
Open cluster observations have shown a bimodal behavior in rotation periods of young, active stars difficult to explain with current models of angular momentum loss. I model stellar winds to understand how the rate at which a star spins down evolves throughout its life.
Stellar winds are a potential threat to close-in exoplanets' atmospheres. I model the coroane of planet hosting stars to asses their "habitability". I have studied the space environment of exoplanets orbiting M-Dwarfs and found that they are likely to suffer from strong atmospheric stripping and evaporation.
The orbital evolution of Cataclysmic Variables (CV), for periods longer than the period gap of ~3.2 hrs, is governed by their angular momentum loss through magnetized winds. I am interested in how the magnetic field alignment and complexity impacts their evolution, potentially explaining the existence of the period gap.
for Stellar Evolution
Neural Networks are known to outperform traditional methods for highly non linear problems. Stellar evolutionary tracks are an are complex and difficult to reverse in order to retrieve fundamental properties from observables like. I have built a fully connected neural network that fully characterizes a star directly from its observed photometry. In addition, I have built a Bayesian Neural Network to determine uncertainties in the model and predictions. I use this to improve the model where it is needed.
Skills & Specialties
Machine learning, artificial neural networks, probabilistic programming, bayesian inference, MHD modeling, large data, parallel computing, stellar astrophysics, black hole physics, and exoplanetary environments.
Python, Pytorch, Pyro, IDL, Fortran, Jupyter Notebooks, Git, Colab.
A Complete Spin-Down Model that reproduces OC observations:
...and also explains the Cataclysmic Variables Period Gap!
Watch my keynote presentation on Stellar Activity and Rotation at the Einstein Fellowship Symposium:
Proxima b and TRAPPIST-1
On the habitability of our temperate terrestrial, rocky neighbors:
Our work suggests that such close-in planets can be exposed to strong dynamic pressure from the stellar wind, 2000 times stronger than the solar wind pressure at Earth or higher, and to fast variations of this pressure over timescales of days, creating conditions for potentially strong atmospheric stripping, heating and possible evaporation.
Simulation of the magnetic environment around the planet TRAPPIST-1 f
In addition, we find that close enough planets do not have magnetospheres protecting them from the intense stellar winds and magnetic pressures. Instead, stellar magnetic field lines connect with the planetary field over most of the planets surface, allowing energetic particles to constantly precipitate on to the atmosphere.
Watch my ITC talk on this "Proxima b and TRAPPIST-1: Check the Space Weather Before Packing"
I enjoy the fresh perspectives that arise from working with students.
I am a mentor at the YouthAstroNet program and for El Universo a Tus Manos Program for Undergraduate Science Majors at CfA, as well as an advisor for the Talented and Gifted STEM program for Latinas in the Boston Public Schools.
In addition, I am a teaching fellow and an invited Lecturer at Harvard, and I have been a teaching assistant for 6 years in Argentina.
The next generation
PhD in Physics, Mar 2010, University of Buenos Aires
M.S. in Astronomy, Dec 2005, National University of La Plata
Research Associate, IACS Harvard University, 2018 to present.
Research Associate, Harvard-Smithsonian CfA, 2018 to present.
Postdoctoral Fellow, Harvard-Smithsonian CfA, 2013 - 2018.
Research Associate at Brandeis University, 2010 - 2013.
Doctoral Fellow of the National Scientific and Technological Research Council Argentina, 2006 - 2010.
Teaching experience of 6 years, mentoring for 3 years.
35 publications in scientific, peer reviewed journals (13 as a first author)
967 citations (as of Aug 2020)
International Invited Speaker at
XXIII Festival de Astronomia
Villa de Leyva Colombia
I sing and play the bass guitar with Esteparios, a group of friends who get together to play latin music.