The BIGS team focuses mainly on stochastic modeling and statistics for methodological purposes, but also aims at a better understanding of biological systems and health phenomena. Its attention is directed to (1) stochastic modeling, (2) estimation and control for stochastic processes, (3) algorithms and estimation for graph data and (4) regression and machine learning. The main objective of BIGS is to exploit these skills in applied mathematics to provide a better understanding of some issues arising in life sciences, with a special focus on (1) tumor growth, (2) photodynamic therapy, (3) genomic data and micro-organisms population study, (4) epidemiology and e-health and (5) dynamics of telomeres.
Online data analysis, Local regression, Estimation of piecewise-deterministic Markov processes (PDMP), Inference for tree data, Network inference, Estimation for complex biological systems, Markovian models for tumor growth, SDEs for bacteriophage systems, MCMC methods for light transport in tissues.
International and industrial relations
Collaboration with Purdue University, CHRU Nancy, Cybernano (Nancy) and Transgene (Strasbourg).