CV

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💼 Work Experience

Incoming Research Scientist (Ph.D.) – Machine Learning
Meta (Modern Recommender Systems AI), 2025–

Postdoctoral Researcher in NeuroAI
New York University, 2020–2025
Supervision: Prof. Xiao-Jing Wang

Scientific Reviewer
Nature Neuroscience, PNAS, Cerebral Cortex, Cognition, PeerJ

Workshop Organizer
Cosyne 2024: Brain-wide modeling in the era of large-scale recordings and high resolution multi-omics

Lecturer & Teaching Assistant
Computational Neuroscience of Cognition, NYU
Methods in Computational Neuroscience, Marine Biological Laboratory (Woods Hole)

Other Roles

• Grant writing contributor (NIH U19, R01, CRCNS)
• Swartz Seminars organizer (2022–2023)
• Lab meeting facilitator (Wang Lab, NYU)
• Physics/Math tutor and former physics library assistant

🎓 Education

Ph.D. in Theoretical (Statistical) Physics
École Normale Supérieure, Paris, 2017–2020
Thesis: Low-dimensional continuous attractors in high-dimensional data: from statistical physics to computational neuroscience
Supervisor: Prof. Rémi Monasson
Distinction: Avec félicitations du jury

M.Sc. in Theoretical Physics
Sapienza University of Rome, 2015–2017
Thesis: Machine learning and phase transitions in the Ising model
Supervisor: Prof. Federico Ricci-Tersenghi
Grade: 110/110 with honors

B.Sc. in Physics
Sapienza University of Rome, 2012–2015
Thesis (in Italian): Dynamics of the bidimensional Ising model
Supervisor: Prof. Giorgio Parisi
Grade: 110/110 with honors

🏆 Awards & Fellowships

Swartz Fellowship in Theoretical Neuroscience
Swartz Foundation, 2022–2025

Spotlight Paper – NeurIPS 2024
Recurrent neural network dynamical systems for biological vision

Physical Review Letters Cover Selection
Capacity-resolution trade-off in the optimal learning of multiple low-dimensional manifolds by attractor neural networks, 2020

HFSP Ph.D. Fellowship
École Normale Supérieure, 2017–2020

Excellence Program Fellowship
Sapienza University of Rome, 2012–2015

🛠 Skills

Programming: Python, C/C++, MATLAB, Mathematica, Julia, R
Libraries/Frameworks: PyTorch, TensorFlow, JAX, Keras, scikit-learn
Scientific Tools: LaTeX, Git, Office Suite
Conceptual Expertise: Computational neuroscience, machine learning, statistical physics, dynamical systems
Languages:

• Italian (native)
• English (fluent)
• French (intermediate)