I am a Research Scientist at the University of Michigan.

I am based in Tokyo, hosted by the RIKEN Center for Quantum Computing (RQC) and the Theoretical Quantum Physics Laboratory at the Cluster for Pioneering Research at RIKEN, Wako. I am also a Visiting Scientist of the RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, where I collaborate on projects in Quantum Computing, Cosmology, and Dark Matter theories.

My academic research is at the interface of computational and theoretical tools, applied to solving the mysteries of the Universe. Current projects span from using deep learning and quantum simulations to understand quantum gravity, to high-performance-computations of subatomic particles in theories that could explain the origin of matter.

I am working on projects related to low-energy nuclear physics, for example calculating nucleon-nucleon interactions or nuclear form-factors directly from the theory of Quantum Chromo-Dynamics (QCD). In addition, I study matrix models in the context of the gauge/gravity duality conjecture with the aim of understanding the possible intriguing relation between gauge theories and quantum gravity. In these projects I apply Machine Learning (ML) approaches to large amount of numerical data available from Monte Carlo simulations with the goal to make new and unexpected discoveries. Recently I also used quantum computing simulations to enable further discoveries on these matrix models.

My expertise is in Markov Chain Monte Carlo (MCMC) numerical simulations of quantum field theories, also known as Lattice Field Theory simulations, which use massively parallel supercomputers (CPU and GPU-based) around the world to solve the complex equations hiding the mysteries of particle physics. MCMC is also used in the field of parameter inference to compute the posterior probabilities of parameters involved in fitting a specific theoretical model to observational or simulated data.

In the past, I worked as an AI Researcher and Engineer at Arithmer Inc., a robotics and artificial intelligence startup in Tokyo, Japan. My research topics were focused around applications of 3D computer vision to enhance automatic measurements and decision making processes based on high-dimensional data. The importance of data and of the inductive bias of specific deep learning architectures were crucial to obtain functional models in these domains. I also worked as an Artificial Intelligence and Computational Physics consultant for other startups in Tokyo and as a teacher for Data Science Bootcamps at Le Wagon Tokyo (trying to inspire the future generation of data scientists!).

Download my resumé.