Data Driven

Obtain and analyze data to inform decisions. Probabilistic Inference. Descriptive Statistics. Time Series Analysis.

Mathematical Modeling

Simplify complex systems using models defined by mathematical equations. Algorithm development.


Python (PyTorch, TensorFlow, Keras), C/C++, Shell, Lua

Team player

Excellent interpersonal skills. Team leader and team player. Outstanding presentation skills


Current and past affiliations

Special Postdoctoral Research Fellow

  • RIKEN Nishina Center - RIKEN (2019 - present)
  • RIKEN BNL Research Center - RBRC (2016 - 2019)

Visiting Research Affiliate

  • Lawrence Berkeley National Laboratory - LBNL (2017 - 2018)

Postdoctoral Researcher

  • Lawrence Livermore National Laboratory - LLNL (2013 - 2016)

JSPS Short-term Fellow

  • Kobayashi-Maskawa Institute, Nagoya University - KMI (2012 - 2012)


Since the early days of my life as a researcher, I have been interested in computational methods applied to physics. Before starting my PhD I performed research in Markov Chain Monte Carlo algorithms, first applied to statistical systems like corrugated surfaces (two-dimensional) and then applied to quantum gauge theories (four-dimensional), with ties to theories of gravity.

I expanded my physics reach during my PhD, studying gauge theories with extra dimensions and quantum field theories with scalars and fermions. Computational methods were still at the center of my research. In particular, I used Lattice Field Theory methods to investigate strongly-coupled theories which are not amenable to perturbative analytical methods. I was interested in gauge theories that could give rise to a particle similar to the Higgs boson, and that could extend our current knowledge of particle physics, currently summarized in the mathematically beautiful Standard Model.

During my first postdoctoral appointment, I continued exploring gauge theories with Higgs candidates, but I also started studying dark matter theories. Using numerical simulations of strongly-coupled theories, I investigated possible dark matter candidates that are tightly-bound heavy composite states, but also elementary dark matter candidates like the axion, which are very light. I also started an independent project about low-dimensional gauge theories connected to quantum gravity in order to test the holographic principle, also called gauge/gravity duality conjecture. The powerful computational resources used in this project allowed me to complete the best test of the holographic principle in 1 dimension, using numerical methods.

Right now I am completing my second postdoctoral appointment during which I have started studying the properties of the neutron from the fundamental theory of its constituents, quarks and gluons. This theory, called Quantum Chromodynamics, can be solved using numerical Lattice Field theory methods but it requires the most powerful supercomputers in the world. One of the achievements is the calculation of the strength of interaction between the neutron and the weak gauge boson of the Standard Model. With an accuracy of 1% we can predict this strength directly from the equations of the fundamental theory, and this can reveal important signals of new physics when confronted with experimental results about the life of neutrons.

The complete list of my publications can be found on inspireHEP.


Composite Dark Matter

Identify new dark matter candidates which are composite and made up of new elementary particles which could be discovered at the LHC

Machine Learning in Physics

Identify directions in machine learning that are useful to theoretical particle physics.

Monte Carlo String/M-theory Collaboration

Develop Monte Carlo methods for Quantum Gravity via Holography

Near-conformal gauge theories with many flavors

Discover properties of strongly-interacting theories with many quarks via numerical Monte Carlo simulations.

Nuclear Physics from Lattice QCD

Understand the properties of hadrons from the fundamental theory of quarks and gluons (QCD).

Recent Publications

. Gauged and Ungauged: A Nonperturbative Test. JHEP, 2019.

Preprint PDF Project

. Neutron-antineutron oscillations from lattice QCD. PRL, 2019.

Preprint PDF Project

. Toward Holographic Reconstruction of Bulk Geometry from Lattice Simulations. JHEP, 2019.

Preprint PDF Project

. A per-cent-level determination of the nucleon axial coupling from quantum chromodynamics. Nature, 2018.

Preprint Code Dataset Project

. Two-Nucleon Higher Partial-Wave Scattering from Lattice QCD. PLB, 2018.

Preprint PDF Project

Recent & Upcoming Talks

The Data Science of Physics
Thu, Jun 13, 2019 4:00 PM
Lattice Composite Dark Matter
Thu, Oct 4, 2018 10:30 AM