Trois journées par an (Lyon, Marseille et Grenoble) avec 4 exposés.
Programme prévisionnel 2025/2026 :
- vendredi 5 décembre 2025 : journée à Marseille
- vendredi 20 mars 2026 : journée à l’UMPA (Lyon)
- lundi 1 juin 2026 (date à confirmer) : journée à Grenoble
Journée 2 à Lyon le 20 mars 2026 (10h30 -16h)
Lieu : UMPA en salle de séminaire 435 (site Monod de l’ENS de Lyon, 4eme étage)
- Inscription (encore possible sauf repas) : https://evento.renater.fr/survey/participation-seminaire-mode-msdybzyb
Planning prévisionnel
Orateurs
- 10h: accueil
- 10h30: Charles Elbar: Including surface tension for tumor growth modeling with the Cahn-Hilliard equation
- 11h30: Maxime Colomb: ICI: Simulations épidémiologiques utilisant un jumeau numérique du territoire et de sa population
- 12h30-14h: buffet en salle passerelle
- 14h: Céline Bonnet: Data-driven mathematical modeling differentiates between cytotoxic and cytostatic effects of nutritive environments
- 15h: Franck Picard: PCA for Point Processes
- Abstract: We are motivated by invasive cancer. A common approach is to use Hele-Shaw models with surface tension, these are models where the tumor is decribed by a set, moving with pressure effects. The surface tension models the cell-to-cell adhesive forces on the boundary of the tumor. However these models may be hard to study / compute numerically, and we can use instead the Cahn-Hilliard equation.
- Abstract: I will present a method to analysis longitudinal flow cytometry and population growth biological data using Bellman Harris stochastic processes (or age-structured Birth and Death processes).
- Abstract: We introduce a novel statistical framework for the analysis of replicated point processes that allows for the study of point pattern variability at a population level. By treating point process realizations as random measures, we adopt a functional analysis perspective and propose a form of functional Principal Component Analysis (fPCA) for point processes. The originality of our method is to base our analysis on the cumulative mass functions of the random measures which gives us a direct and interpretable analysis. Key theoretical contributions include establishing a Karhunen-Loève expansion for the random measures and a Mercer Theorem for covariance measures. We establish convergence in a strong sense, and introduce the concept of principal measures, which can be seen as latent processes governing the dynamics of the observed point patterns. We propose an easy-to-implement estimation strategy of eigenelements for which parametric rates are achieved. We fully characterize the solutions of our approach to Poisson and Hawkes processes and validate our methodology via simulations and diverse applications in seismology, single-cell biology and neurosiences, demonstrating its versatility and effectiveness. Our method is implemented in the pppca R-package.
Journée soutenue par l’UMPA