I am researching methods for improving the representations learned by machine learning models through the use of uncertainty and information sharing. Uncertainty is needed because real-world data is noisy and our modeling assumptions are imperfect. Complementary, information sharing techniques like transfer learning and multi-view learning allow models to learn from diverse sources. The machine learning tools I am using in my research come from the Bayesian probabilistic or the deep learning domain, but I also investigate methods combining these areas. I have worked in a variety of application areas, such as: multi-modal information fusion, decision making, information retrieval, AI-assisted data science.