Mattias Villani is Professor of Statistics and head of the Division of Statistics and Machine Learning at Linköping University, Sweden. He is also Professor of Statistics at Stockholm University. He leads the Machine Learning Research Group, and is a scientific consultant for the Central Bank of Sweden. Mattias Villani research focuses on developing computationally efficient Bayesian methods for inference, prediction and decision making using flexible probabilistic models. His current application areas are neuroimaging, robotics, text analysis and econometrics.
In the last decade or so, there has been a dramatic increase in storage facilities and the possibility of processing huge amounts of data. This has made large high-quality data sets widely accessible for practitioners. This technology innovation seriously challenges inference methodology, in particular simulation algorithms commonly applied in Bayesian inference. These algorithms typically require repeated evaluations over the whole data set when fitting models, precluding their use in the age of so called big data.
Invited talks, refereed proceedings and other conference outputs
Dang, K.. D., Quiroz M., Kohn R., Tran M N., & Villani M.
(2018). Hamiltonian Monte Carlo with energy conserving subsampling. The 2nd International Conference on Econometrics and Statistics (EcoSta 2018).
Quiroz, M., Tran M N., Villani M., Kohn R., & Dang K-D.
(2018). The block-Poisson estimator for optimally exact subsampling MCMC. Presented at the International Society of Bayesian Analysis.
Magnusson, M., Jonsson L., Villani M., & Broman D.
(2017). Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models. Journal of Computational and Graphical Statistics. doi: 10.1080/10618600.2017.1366913