Hello!

I am Francisco Villaescusa-Navarro, mostly known as Paco. I am a research scientist at the Simons Foundation in New York City and a visiting research scholar at Princeton University. I did my Ph.D. at the Instituto de Fisica Corpuscular in Valencia, Spain. I have been a visiting graduate student at the Canadian Institute for Theoretical Astrophysics (CITA) and the Institute for Theory and Computation (CfA/Harvard University). After completing my Ph.D. I was a postdoctoral researcher at the Astronomical Observatory of Trieste, Italy, and a Flatiron research fellow at the Center for Computational Astrophysics in New York. Later, I went to Princeton University as an associate research scholar.

I am a theoretical astrophysicist working on several aspects of cosmology. I investigate the properties of the large-scale structure of the Universe with the goal of using cosmological observations to learn about fundamental physics. The goal of my research is to develop the tools that will allow to extract every single bit of cosmological information from cosmic surveys. To achieve this, I combine machine/deep learning techniques with very large sets of state-of-the-art numerical simulations. I am particularly interested in understanding the impact of neutrino masses on cosmological observables, that I study combining analytic techniques and state-of-the-art numerical simulations. My research also involves investigating how cosmological and astrophysical information can be extracted from 21cm intensity mapping surveys. I have also worked on other topics such as the Lyman-alpha forest, modified gravity, BAO reconstruction, kSZ and galaxy formation and evolution. More recently, my research interests have been moving to machine learning, and its usage in cosmology.

I am the main architect of the Quijote simulations: a suite of 44,110 full N-body simulations containing trillions of particles over a combined cosmological volume larger than the volume of the entire observable Universe. I also developed its precursor, the HADES simulations. I am part of the core team who designed and developed the Cosmology and Astrophysics with MachinE Learning Simulations, CAMELS, a suite of thousands of state-of-the-art cosmological (magneto-)hydrodynamic simulations designed to train machine learning algorithms. I am also the main developer of the Pylains libraries, a set of python, cython and C libraries designed to analyze numerical simulations.

I co-lead the CAMELS project. I am a member of Euclid consortium, where I co-lead the Machine Learning for Cosmological Simulations WP. I am also part of the PFS, WFIRST, Square Kilometre Array, and SMAUG collaborations.