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.

  • Quantum Physics
  • Particle Physics
  • Lattice Field Theory
  • Dark Matter
  • Quantum Gravity
  • Numerical Simulations
  • Data Analysis
  • Deep Learning
  • Quantum Computing
  • PhD in Theoretical Particle Physics, 2013

    University of Edinburgh

  • MSc in Theoretical Physics, 2009

    University of Milan

  • BSc in Physics, 2007

    University of Milan


Current and past affiliations

Research Scientist
Mar 2021 – Present Ann Arbor, MI
Research on computational methods to advance scientific discovery. Responsibilities include publishing papers, writing research grants, supervise students.
Visiting Research Scientist
May 2022 – Present Wako

Research on quantum algorithms for quantum field theory, Projects are related to:

  • Matrix Models
  • Lattice Field Theories
  • Variational Quantum Algorithms
  • Quantum Information
Visiting Research Scientist
Mar 2021 – Present Wako

Research on open quantum systems and combinatorial optimization, taking advantage of machine learning and quantum computing simulations. Projects are related to:

  • Dynamics of OQS
  • Matrix Models
  • Graph Neural Networks for CO
  • Scientific ML
  • Simulation-based Inference
Visiting Research Scientist
Sep 2019 – Present Wako
Research on “Quantum Computing and Machine Learning” for discoveries in physics through computational advantage. Responsibilities include organizing seminars in working groups, writing papers, initialize collaborations on new scientific research directions.
AI Researcher/Engineer
Oct 2019 – Feb 2021 Tokyo

Working on state-of-the-art deep learning algorithms in 3D Computer Vision for robotics and computer graphics. Responsibilities included:

  • Research
  • Data exploration
  • Model experiments
  • Deployment
Special Postdoctoral Research Fellow
Apr 2019 – Sep 2019 Wako
Special Postdoctoral Research Fellow
Oct 2016 – Mar 2019 New York
Postdoctoral Researcher
Oct 2013 – Sep 2016 California
JSPS Short-term Fellow
Apr 2012 – Dec 2012 Nagoya


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), Julia, C/C++, Fortran

Team player

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


A brief overview of my research topics.

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.

In my second postdoctoral appointment, 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.

My current research topics are in the field of scientific machine learning, that is applications of artificial intelligence techniques to solve difficult physics problems or accelerate scientific discoveries. The complete list of my publications can be found on inspireHEP.


Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization
Machine Learning in production using GCP (Coursera Specialization)
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Image Understanding with TensorFlow on GCP
Machine Learning on large image datasets using GCP (Tensorflow-based)
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Sequence Models for Time Series and Natural Language Processing
Machine Learning on time series (including NLP applications)
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Recommendation Systems with TensorFlow on GCP
Machine Learning for recommendation systems (in production on GCP)
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Getting Started with Google Kubernetes Engine
Fundamentals of Kubernetes (integrated on GCP)
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Production Machine Learning Systems
Machine Learning in production
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End-to-End Machine Learning with TensorFlow on GCP
Machine Learning in production using GCP (Tensorflow-based)
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How Google does Machine Learning
Introduction to machine learning on GCP
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Databases and SQL for Data Science
IBM Data Science. Skills: Cloud Databases, SQL, Python, Jupyter
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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).