Japan, Kyoto, Golden pavilion
Computational Statistical Learning
Bayesian Inverse Problems
Stochastic algorithms
Monte Carlo methods
Markov chains
Monte Carlo methods: Adaptive Importance Sampling, Adaptive Biasing Force methods for Monte Carlo sampling, Markov chain Monte Carlo (MCMC) methods, Adaptive MCMC, Interacting MCMC, Sequential Monte Carlo.
Stochastic optimization: Monte Carlo Expectation Maximization (MCEM), Stochastic Approximation Expectation Maximization, Online MCEM, Stochastic Approximation with Markovian inputs, Stochastic Gradient, Perturbed Proximal-Gradient, Perturbed Nesterov-based accelerations, Distributed Stochastic Approximation, Federated Expectation Maximization, Stochastic Majorize-Minimization algorithms.
Markov chain theory: sub-exponential ergodicity by drift inequalities, explicit control of convergence, limit theorems for adaptive Markov chains. Applications to the theoretical analysis of MCMC samplers.
Bayesian Inverse problems applied to: microarray classification, image segmentation, simultaneous localization and mapping, inference of cosmological parameters, kriging-based approach for cellular coverage analysis, Covid-19 reproduction number estimation.
