Research Scholar, University of Chicago.
I work on the statistical physics of extreme values and strongly correlated particle systems — stochastic resetting, random matrix theory, order statistics — and, increasingly, on where those tools meet machine learning. Previously a PhD student at LPTMS, Université Paris-Saclay, under Satya Majumdar.
N Brownian particles on a line, each simultaneously reset to the origin at rate r. The shared resetting event alone correlates otherwise-independent particles; we derive the resulting order-statistics and spacing distributions in closed form.
A gas of Brownian particles whose collective reset is triggered by a first-passage event — the first one to reach a threshold drags the others back. Correlations, stationary density, and extreme-value statistics in this first-passage-coupled setting.
Generalizes the correlated Brownian gas to non-Poissonian resetting protocols — deterministic, power-law, heavy-tailed. The shape of the inter-reset distribution reshapes the correlation structure.
An optical-tweezers experiment that directly measures the emergent correlations predicted for particles in a stochastically switching trap — the first experimental realization of the mechanism.
Dyson Brownian motion of N eigenvalues, reset simultaneously. Stationary density, extreme-eigenvalue statistics, and the crossover between resetting-dominated and repulsion-dominated regimes.
Why this redesign, and what to expect here.