Research

Machine learning theory and stochastic analysis

My work connects deep-learning theory, high-dimensional probability, human-AI decision making and stochastic dynamics.

Preprints

  1. Preprint From Information to Delegation: Mapping Human-AI Financial Decision Making arXiv:2608.02100
  2. Preprint Universality in Deep Neural Networks: An approach via the Lindeberg exchange principle arXiv:2605.02771
  3. Preprint Non-selection of Lagrangian trajectories in the zero-noise limit for a class of stochastic regularizations arXiv:2606.07096
  4. Preprint A uniform particle approximation to the Navier-Stokes-alpha models in three dimensions with advection noise arXiv:2504.12960
  5. Preprint A uniform point vortex approximation for the solution of the two-dimensional Navier Stokes equation with transport noise arXiv:2410.23163

Reading group note

Stochastic perturbations of transport equations

A review of the work of Flandoli, Gubinelli and Priola (2010), prepared for a reading group at Imperial College London.

Read the note (PDF)