• Markov stochastic processes, with discrete/continuous time (ergodicity, coupling, mixing)
• Representation as random dynamical system
• Interacting particle system, probabilistic cellular automata (PCA), interacting Markov chains
• Statistical mechanics: Gibbs measures, phase transition
• Stochastic processes with values in a combinatorial space, random planar tilings
• Stochastic algorithm: MCMC, perfect sampling, extreme distributions
• Stochastic models for physics and biology
• Stochastic Simulation, statistical data analysis (Scilab, R)
• Use of discrete math. softwares: graphs, combinatorics (Softwares)