About

Matteo Cagnotti

I am a second-year PhD student in Modeling and Data Science under the supervision of Elena Issoglio at the University of Turin.

My research is about the numerical approximation of stochastic differential equations with singular drift, either through rough martingale problems for Brownian-noise SDEs or through regularization by noise techniques for fractional Brownian-noise SDEs.

During the spring of 2026 I will be working in collaboration with Rémi Catellier while visiting Université Côte d'Azur, Nice.

Matteo Cagnotti
Research

Research

I study SDEs whose drift is too singular to be interpreted pointwise.

  • Martingale problems with distributional drift
  • Backward PDEs with rough coefficients
  • Weak error estimates for numerical schemes
  • Fractional Brownian variants and local-time-type drifts
Papers

Papers

Lp-sup convergence of the Euler-Maruyama scheme for SDEs with distributional Besov drift

Preprint, 2026. I prove convergence rates in Lp, for all p ≥ 2, for the Euler-Maruyama scheme applied to one-dimensional Brownian SDEs with drift in a negative-order Besov space. The proof uses the Yamada-Watanabe approximation technique and gives an explicit L1-sup convergence rate.

arXiv:2602.02109math.PRsubmitted 2 Feb 2026

Martingale Problems with Distributional Drift: Convergence of the Euler Scheme

Work in progress. Check back later for updates.

Numerical Schemes for Skew Fractional Brownian Motion

Work in progress. Check back later for updates. In collaboration with Rémi Catellier.

Contact
Matteo Cagnotti