Theodore Papamarkou

FR-Adjunct Professor

Biography

My research spans probabilistic machine learning. I am interested in addressing questions related to uncertainty quantification and to approximate inference with big data or with high-dimensional models. My two main research projects pertain to approximate Markov chain Monte Carlo (MCMC) for Bayesian deep learning and to neural network Gaussian processes (NNGPs).