Welcome

The LPSM is a research unit jointly supported by CNRS, Sorbonne Université and Université Paris Cité. The unit hosts about 200 members (about 90 faculty) and is located at two sites (Campus P. et M. Curie of Sorbonne Université et Campus Paris Rive Gauche of Université Paris Cité).

The LPSM research activities cover a broad spectrum in Probability and Statistics, from the most fundamental aspects (which, in particular, include Stochastic Analysis, Random Geometry, Numerical Probabilities and Dynamical Systems) to applications in the Modelling in various disciplines (Physics, Biology, Data Sciences, Finance, Insurance, etc). Applications involve partnerships with the non-academic sector.

While the unit LPSM is relatively recent, its components have deep roots in the rich history of the “mathematics of randomness” that has unfolded in Paris during the 20th century (see here for more details).

NB: This website is largely inspired by the one of IRIF.

9.12.2025
L'équipe composée de Claire Boyer (Saclay), Francis Bach (Inria) et Gérard Biau (LPSM) est lauréate de l'AAP “Mathématiques de l'apprentissage profond” du PEPR IA, pour le projet Géné-Pi. Félicitations!

5.2.2026
Nous apprenons avec tristesse le décès de Paul Deheuvels, membre de l'Académie des Sciences, professeur émérite à Sorbonne Université et membre du LPSM, survenu le 30 janvier 2026. Voici une notice biographique en français, et en anglais.

9.12.2025
L'Académie des Sciences a décerné à Nicole El Karoui la médaille de section “Applications des Sciences”. Félicitations!

toutes les anciennes actualités

(Ces actualités sont présentées selon un classement mêlant priorité et aléatoire.)

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Séminaire de Probabilités
Mardi 10 mars 2026, 14 heures, Jussieu, Salle Paul Lévy, 16-26 209
Ellen Powell (Durham University) Scaling limits of critical FK-decorated maps at q=4

The critical Fortuin–Kasteleyn random planar map with parameter q>0 is a model of random (discretised) surfaces decorated by loops, related to the q-state Potts model. For q<4, Sheffield established a scaling limit result for these discretised surfaces, where the limit is described by a so-called Liouville quantum gravity surface decorated by a conformal loop ensemble. At q=4 a phase transition occurs, and the correct rescaling needed to obtain a limit has so far remained unclear. I will talk about joint work with William Da Silva, XinJiang Hu, and Mo Dick Wong, where we identify the right rescaling at this critical value and prove a number of convergence results.

Séminaire doctoral du LPSM
Mardi 10 mars 2026, 17 heures 30, Sophie Germain - Salle 1016 (1er étage)
Sobihan Surendran + Paul Mella Latent Guided Sampling for Neural Combinatorial Optimization (S. Surendran) + Understanding chaotic flows in terms of invariant probability measures

Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging. Recent Neural Combinatorial Optimization (NCO) methods leverage deep learning to learn policies for constructing solutions, trained via supervised or reinforcement learning (RL). While promising, these methods often perform poorly on out-of-distribution instances and lack robust inference mechanisms. Moreover, existing latent-space models either require labeled data or rely on an instance-independent latent structure. In this work, we introduce an instance-conditioned latent space model trained via RL, enabling sampling-based inference beyond standard decoding methods. Building on this representation, we propose an efficient inference method, Latent Guided Sampling (LGS), based on Markov chain Monte Carlo coupled with stochastic approximation for test-time adaptation toward lower-cost solutions. We also propose a diffusion prior over the latent space, which improves performance at the expense of additional computation. We show that the iterations of our method form a time-inhomogeneous Markov chain and provide rigorous theoretical convergence guarantees. Empirical results on benchmark routing tasks show that our method achieves state-of-the-art performance among NCO baselines.

The geodesic flow on a negatively curved Riemannian manifold is a classical example of a hyperbolic (i.e. chaotic) dynamical system. A remarkable property of this flow is that the space of its invariant probability measures has a very specific structure, known as the Poulsen simplex. To some extent, this structure still holds when the curvature of the manifold is only assumed to be non-positive rather than negative, suggesting that this structure is not directly tied to the hyperbolicity of the flow, but rather to some weaker property.

Mathématiques financières et actuarielles, probabilités numériques
Jeudi 12 mars 2026, 11 heures 15, Sophie Germain salle 1013
Ahmed Kebaier (LAMME, Univer Paris Saclay) Non encore annoncé.

Les probas du vendredi
Vendredi 13 mars 2026, 11 heures, Jussieu, Salle Paul Lévy, 16-26 209
Guillaume Blanc à venir

Séminaire sur les processus de Hawkes
Lundi 16 mars 2026, 14 heures, Jussieu, Paul Lévy 16-26 second floor
Grégoire Szymanski (Université de Luxembourg) Mean-Field Limits for Nearly Unstable Hawkes Processes

Séminaire du LPSM
Jeudi 19 mars 2026, 9 heures 30, Amphi 55A
Richard Nickl (University of Cambridge) Statistical inference for infinite-dimensional dynamical systems

A common task in `data assimilation’ is to assign a Gaussian process prior on the initial condition of a dynamical system and to update it to a Bayesian posterior measure in the space of possible trajectories given a discrete sample of the process. In many important applications the dynamics are non-linear, such as with Navier-Stokes equations in geophysical sciences or reaction-diffusion equations in biochemistry. While Bayesian posterior distributions are widely computed by filtering or MCMC methods, almost nothing is known about the statistical behaviour of these posterior measures in non-linear setting. In this talk we will introduce the framework and then present recent results, known as `Bernstein-von Mises theorems’, that show that the posterior measures are approximated in function space by the Gaussian laws of solutions to certain SPDEs that involve the inverse Fisher information of the underlying statistical model.

Les probas du vendredi
Vendredi 20 mars 2026, 11 heures, Jussieu, Salle Paul Lévy, 16-26 209
Corentin Faipeur (ENS Lyon) à venir

Séminaire de Probabilités
Mardi 24 mars 2026, 14 heures, Jussieu, Salle Paul Lévy, 16-26 209
Oriane Blondel (LPSM, CNRS) A venir

Séminaire doctoral du LPSM
Mardi 24 mars 2026, 17 heures 30, Jussieu, Salle Paul Lévy, 16-26 209
Eyal Cohen + Maxence Petit TBA + TBA

Séminaire Modélisation et Probabilités
Mercredi 25 mars 2026, 14 heures 15, Sophie Germain 1013
Arno Kuijlaars (KU Leuven) Non encore annoncé.

Mathématiques financières et actuarielles, probabilités numériques
Jeudi 26 mars 2026, 11 heures 15, Sophie Germain salle 1013
Alexandre Richard (Centrale Supélec) Non encore annoncé.

Les probas du vendredi
Vendredi 27 mars 2026, 11 heures, Jussieu, Salle Paul Lévy, 16-26 209
Isao Sauzede (ENS Lyon) à venir

Séminaire Modélisation et Probabilités
Mercredi 1 avril 2026, 14 heures 15, Sophie Germain 1013
Ronan Memin (DMA (ENS, Paris)) Non encore annoncé.

Séminaire Modélisation aléatoire du vivant
Mercredi 1 avril 2026, 11 heures, 16-26.209
Luis Almeida (LPSM) Non encore annoncé.

Les probas du vendredi
Vendredi 3 avril 2026, 11 heures, Jussieu, Salle Paul Lévy, 16-26 209
Joseph Chen à venir

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