### Research Interests

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 started to work on the dynamics of scaling limits of machine learning algorithms seen as interacting particle systems.

- Large deviations with Markov jump processes with uniformly diminishing rates, with JL. Andreis, M. Renger, R. Patterson,
**arXiv:2102.13040**
- 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 (to appear)**
- Temporal Difference Learning with nonlinear function approximation in the lazy training regime, with J. Lu,
**arXiv:1905.10917**
- 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)

### Teaching

- Stochastic calculus (MATH 545.01)