Marco Biroli

Marco Biroli

Research Scholar (postdoctoral) · University of Chicago, since 2025.

I work at the interface of theoretical physics and machine learning. Most strongly correlated systems are intractable; I look for the exceptions — the ones where correlations are driven by a handful of hidden variables.

Fig. 1 — resetting brownian gas
FIG. 1 N non-interacting diffusers on the real line, reset together at Poisson times (vertical marks). The reset events alone — no direct interaction — couple them; the apparent independence is, by construction, false. Biroli, Larralde, Majumdar & Schehr, PRL 130, 207101 (2023).

Research

01 — MACHINE LEARNING

The physics of learning

Where statistical mechanics meets generative modeling. Why a VAE is structurally a finite-size mean-field model, and what that costs when the data isn't — with the 2D Ising transition as a clean test case.

→ VAEs as finite-size mean-field models
02 — CORRELATED SYSTEMS

Dynamically emergent correlations

Non-interacting particles coupled only by a shared reset event — enough to correlate a gas that would otherwise never meet. Exact results for extremes, gaps, and order statistics follow from conditioning on a single hidden variable.

→ Dynamically emergent correlations in switching traps→ Experimental evidence for emergent correlations→ Extreme statistics in a resetting Brownian gas→ Exact extreme, order & sum statistics
03 — RANDOM MATRICES

Spectra under resetting

Dyson Brownian motion of eigenvalues, reset simultaneously. Stationary density, extreme-eigenvalue statistics, and the crossover between repulsion- and reset-dominated regimes.

→ Resetting Dyson Brownian motion

Currently

A VAE is a mean-field model in disguise.

The conditional-independence assumption baked into every VAE decoder — pθ(x | z) = Πi p(xi | z) — is formally equivalent to a finite-size mean-field factorization.

The consequence: a VAE perfectly recovers Curie–Weiss systems, but structurally fails on genuinely correlated ones. Trained on 2D Ising samples, it smears out the sharp singularity at Tc ≈ 2.27. No amount of training recovers it.

News

2026-03-03

Schmidt AI in Science seminar at UChicago

Presented Variational auto-encoders are finite-size mean-field approximators at the Schmidt AI in Science Speaker Series, University of Chicago.

2025-09-08

Emergent correlations beyond Poissonian resetting

With Gabriele de Mauro, Satya N. Majumdar and Grégory Schehr — how non-Poissonian reset protocols reshape the correlation structure of the resetting gas. Now published in Phys. Rev. E 113, 014120 (2026).

2025-09-01

Joined the University of Chicago

Started as a Research Scholar across the Physics and Computer Science departments, working with Vincenzo Vitelli — supported by the Eric & Wendy Schmidt AI in Science fellowship.

2025-08-18

PhD thesis on arXiv

Strongly correlated stochastic systems — the doctoral thesis behind the resetting-gas line of work. LPTMS, Université Paris-Saclay; advisor Satya N. Majumdar.

2025-08-10

Emergent correlations, observed

An optical-tweezers experiment with the ENS Lyon group directly measures the correlations predicted for particles in a switching trap — the first experimental realization of the mechanism.

Publications

239 citations  ·  h-index 8 — Google Scholar, 2026-06-10

2026
First-passage resetting gas
Marco Biroli, Satya N. Majumdar, Grégory Schehr
Europhysics Letters 153, 31002
cited 4× arXiv DOI
2026
Dynamically emergent correlations in Brownian particles subject to simultaneous non-Poissonian resetting protocols
Gabriele de Mauro, Marco Biroli, Satya N. Majumdar, Grégory Schehr
Physical Review E 113, 014120
cited 10× arXiv DOI
2025
Experimental evidence for strong emergent correlations between particles in a switching trap
Marco Biroli, Sergio Ciliberto, Manas Kulkarni, Satya N. Majumdar, Artyom Petrosyan, Grégory Schehr
arXiv preprint
cited 7× arXiv
2025
Resetting Dyson Brownian motion
Marco Biroli, Satya N. Majumdar, Grégory Schehr
Physical Review E 112, 014101
cited 14× arXiv DOI
2025
Strongly correlated stochastic systems
Marco Biroli
arXiv preprint (PhD thesis / review)
cited 1× arXiv
2024
Exact extreme, order, and sum statistics in a class of strongly correlated systems
Marco Biroli, Hernán Larralde, Satya N. Majumdar, Grégory Schehr
Physical Review E 109, 014101
cited 26× arXiv DOI
2024
Resetting by rescaling: exact results for a diffusing particle in one dimension
Marco Biroli, Yannick Feld, Alexander K. Hartmann, Satya N. Majumdar, Grégory Schehr
Physical Review E 110, 044142
cited 12× arXiv DOI
2024
Dynamically emergent correlations between particles in a switching harmonic trap
Marco Biroli, Manas Kulkarni, Satya N. Majumdar, Grégory Schehr
Physical Review E 109, L032106
cited 32× arXiv DOI
2023
Critical number of walkers for diffusive search processes with resetting
Marco Biroli, Satya N. Majumdar, Grégory Schehr
Physical Review E 107, 064141
cited 30× arXiv DOI
2023
Extreme statistics and spacing distribution in a Brownian gas correlated by resetting
Marco Biroli, Hernán Larralde, Satya N. Majumdar, Grégory Schehr
Physical Review Letters 130, 207101
cited 67× arXiv DOI
2022
Number of distinct sites visited by a resetting random walker
Marco Biroli, Francesco Mori, Satya N. Majumdar
Journal of Physics A 55, 244001
cited 28× arXiv DOI
2022
Time, privacy, robustness, accuracy: trade-offs for the open vote network protocol
Fatima-Ezzahra El Orche, Rémi Géraud-Stewart, Peter B. Rønne, Gergei Bana, David Naccache, Peter YA Ryan, Marco Biroli, Megi Dervishi, Hugo Waltsburger
E-Vote-ID 2022 (LNCS 13553)
cited 8× DOI

Contact

I'm happy to hear from anyone working on correlated noise, exact results, or the statistical physics of learning. Email is fastest.

marco.biroli@gmail.com
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