I am interested in applied probability theory, more specifically in interacting particle systems for real world applications. I have worked on scaling limits for models of chemical reaction networks, focusing on the relations between their dynamics and their structure. More recently, I have worked on the dynamics of scaling limits of machine learning algorithms seen as interacting particle systems, and on dynamics of fluid models.

- Scalable bayesian inference for the generalized linear mixed model, with S. Berchuk, F. Medeiros, and S. Mukherjee,
**arXiv:2403.03007** - Fair Artificial Currency Incentives in Repeated Weighted Congestion Games: Equity vs. Equality , with L. Pedroso, M. Heemels, and M. Salazar,
**arXiv:2403.03999** - Random Splitting of Point Vortex Flows, with F. Grotto and J. Mattingly,
**arXiv:2311.15680** - Global optimality of Elman-type recurrent neural networks in the mean-field regime, with J. Lu and S. Mukherjee,
**International Conference on Machine Learning (2023)** - Random Splitting of Fluid Models: Positive Lyapunov Exponents, with J. Mattingly and O. Melikechi,
**arXiv:2210.02958** - Random Splitting of Fluid Models: Ergodicity and Convergence, with J. Mattingly and O. Melikechi,
**Communications in Mathematical Physics (2023)** - A homotopic approach to policy gradients for linear quadratic regulators with nonlinear controls, with C. Chen,
**IEEE Proceedings of of Conference on Decision and Control (2022)** - Large deviations with Markov jump processes with uniformly diminishing rates, with L. Andreis, M. Renger, R. Patterson,
**Stochastic Processes and Their Applicatons (2022)** - Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime, with J. Lu,
**International Conference on Learning Representations (2021)** - Temporal Difference Learning with nonlinear function approximation in the lazy training regime, with J. Lu,
**Proceedings of Machine Learning Research, Mathematical and Scientific Machine Learning (2021)** - Seemingly stable chemical kinetics can be stable, marginally stable or unstable, with J. Mattingly,
**Comm. Math. Sci. 18 (6)**, 1605 - 1642**(2020)** - Large Deviations Theory for Markov Jump Models of Chemical Reaction Networks, with A. Dembo and J.-P. Eckmann,
**Ann. Appl. Prob. 28 (3)**, 1821-1855**(2018)** - On the Geometry of Chemical Network Theory: Lyapunov Function and Large Deviations Theory, with A. Dembo and J.-P. Eckmann,
**J. Stat. Phys. 172 (2)**, 321-352**(2018)** - The Colored Hofstadter Butterfly for the Honeycomb Lattice, with G. M. Graf and J.-P. Eckmann,
**J. Stat. Phys. 156 (3)**, 417-426**(2014)**

- (Deep) Learning Theory (PhD course in mathematics, 2023/24)
- Statistica I (Ingegneria Gestionale, a.a. 2023/24)
- Statistica Matematica (Matematica, a.a. 2023/24)