Affiliation: Instituto de Física Corpuscular (IFIC), CSIC-Universitat de València

Fields or areas of research Dark Matter phenomenology, Machine Learning for High Energy (Astro)Physics, pure Machine Learning research, Quantum Computation

I have done my bachelor studies in Nuclear Physics at the The Higher Institute of Technologies and Applied Sciences (InSTEC), Havana, Cuba. I did my Ph.D studies at the Autonomous University of Madrid (UAM). My thesis focused mainly on Dark Matter (DM) phenomenology, as well as the development of theories "beyond the Standard Model" containing DM candidates whose production happened outside thermal equilibrium. After finishing my Ph.D, in 2013 I went for a first postdoc at the Université Libre de Bruxelles, Belgium, and later on a second postdoc at the CNRS, in Annecy, France. During that period I developed, among other things, the phenomenological potential of non-thermal DM candidates, and was the co-author of the first public code able to compute the relic abundance of non-thermal DM candidates, for a generic model (cf. "Micromegas v.5"). In 2018 I came back to UAM as a fellow of the "Atracción de Talento" program of the Comunidad de Madrid, before starting at IFIC, Valencia, in 2021, as CIDEGENT fellow. In the last 4 years my research has focused more and more in the data, where I have applied existing and developed new Machine Learning (ML) methods to: improve the Indirect Detection searches of DM, search for "new physics" at the LHC, perform neutrino event reconstruction in the Super-Kamiokande Collaboration, perform gamma-ray event reconstruction in the Cherenkov Telescope Array (CTA), as we as doing pure ML research, among others. Finally, in the last year I started to work in the field of quantum computation, and the development of the new research line called "quantum Machine Learning".