My work in Bayesian machine learning includes the design and implementation of scalable methods for approximate inference and the construction, evaluation and refinement of probabilistic models that successfully describe the statistical patterns present in the data. During the last years I have designed new Bayesian machine learning methods with applications to the prediction of customer purchases in on-line stores, the modeling of price changes in financial markets, the analysis of the connectivity of genes in biological systems, the discovery of new materials with optimal properties or the design of more efficient hardware. I have focused on approaches based on probabilistic models, relying on methods for approximate inference that scale to large datasets. The results of this research have been published at top machine learning journals (Journal of Machine Learning Research) and conferences (NIPS and ICML).