I am a PhD student in Applied Mathematics under the joint supervision of Eric Moulines, professor at the CMAP laboratory, Ecole Polytechnique, and Alain Durmus, associate professor at the CMLA laboratory, ENS Paris-Saclay.

**2019-:**PhD in Applied Mathematics: Stochastic algorithms for learning and optimization on Riemannian manifolds, at CMAP - Ecole Polytechnique**April-September 2019:**Internship at CMLA laboratory, under the direction of Alain Durmus, about optimal scaling of MCMC algorithms**2018-2019:**Master of Science in Mathematics and applications: Probabiality and Statistics, Université Paris-Sud, Orsay**2017-2018:**Master of Science in Mathematics teaching, ENS Paris-Saclay, Cachan**2015-2017:**Bachelor and first year of Master of Science in Mathematics and applications, Université Paris-Sud, Orsay**2013-2015:**Undergraduate studies in Science, specially Mathematics and Physics (Classe préparatoire MP), Lycée Louis-le-Grand, Paris

I'm working on algorithms for optimization on Riemannian manifolds, and optimal scaling of MCMC algorithms.

There is a current trend in which statistical learning methods are used in conjunction with differential geometric techniques. One of the main ideas behind these models is to assume that the observations belongs to some Riemannian manifold.

Our main goal is to develop a coherent framework for:

**Stochastic approximation**, including stochastic gradient descent methods**Stochastic processes and Monte-Carlo simulation**, which are useful for applied learning and optimization problems

You can check my presentation of my master's thesis here.

In this presentation you can find a brief introduction to Bayesian neural networks, and my work on MCMC in english.

**2019-:**Organizer and teacher in probability and statistics, ENS Paris-Saclay, M2 FESUP.

CMAP

Ecole Polytechnique

Route de Saclay

91128 Palaiseau

pablo.jimenez-moreno at polytechnique.edu