Felix Dangel is interested in computing and using information beyond the gradient for deep learning. During his PhD with Philipp Hennig in Tuebingen, he developed tools to efficiently compute quantities beyond the gradient, like gradient statistics over a mini-batch and Hessian approximations. As a Postdoc at Vector, he is further exploring the potential of such higher-order information for building more powerful deep learning methods. Felix holds a Bachelor and Master degree in Physics from the University of Stuttgart where he worked on the numerical simulation of quantum many-body systems.