I am a first year PhD student in Philipp Hennig's PN-Group. My PhD is funded through a grant by ETAS (Bosch), which offers me the opportunity to gain insight into issues of machine learning in 'real life' - applications, especially for embedded-systems. My work aims for probabilistic model-adaption in deep learning during stochastic optimization.

My research:

The choice of network architecture is an essential step in deep learning on which the performance of the model depends crucially. Most model adaption techniques are based on `outer loop' optimization (e.g. Bayesian optimization, cross-validation, etc.), which have high computational costs and are difficult to control. In my project I study ways to adapt the model directly within the `inner loop’ of the stochastic optimization algorithm that also fits the model itself. We exploit statistics (e.g. the element-covariance) of the stochastic gradients within the batch to separate relevant from irrelevant model parts.

About me:

Prior to joining the MPI, I studied Physics in Stuttgart (Germany) and spent time abroad as an exchange student at Durham University (England). I did my Master's project on diffusion of DNA-grafted colloidal particles in crowded environments, at another research group at the MPI.