In March 2017, I joined the Probabilistic Numerics group where we tackle some of the core aspects underlying the field of Machine Learning from a probabilistic perspective.

A central component of Machine Learning is the training step which involves finding an optimal parameter configuration w.r.t. a loss function. Due to the computational complexity of optimizing the parameters, computationally cheaper 1st order methods (SGD) are often preferred over more accurate 2nd order methods (Newton, CG). I hope to bridge the trade-off between speed and accuracy by extracting and transferring important information for 2nd order methods to make them converge faster, thus reducing the overall computational cost.

Prior to joining the MPI, I finished my studies in Engineering Physics at Chalmers University of Technology in Göteborg, Sweden. The programme consisted of a B.Sc. in Physics and a M.Sc. in Complex Adaptive Systems, of which the first year was spent at TU Delft.