Research interests lie in the areas of machine learning, uncertainty estimation, and generative models. Currently interested in an 'understanding-through-synthesis' approach, leveraging generative models combined with interpretable priors, e.g., 3D geometry in computer vision and robotics, or physical constraints in drug discovery.