My main research interest lies in the foundation of usable, scalable and efficient data-centric systems to support all kinds of interactions in a machine learning model lifecycle, broadly known as MLOps. This notably led to the definition of new engineering principles, such as how to run a feasibility study for ML application development, or how to perform continuous integration (CI) of ML models with statistical guarantees. I'm furthermore working on efficient methods to enable a model search functionality in pre-trained model collections. This typically serves as a starting point to solve new machine learning tasks using transfer learning.
I hold a bachelor's degree from the Bern University of Applied Sciences and received my MSc in Computer Science from ETH Zurich in 2018. My work on Efficient Sparse AllReduce For Scalable Machine Learning was awarded with the silver medal of ETH Zurich for outstanding master thesis.
During my PhD, I have worked as a research intern and student research consultant at Google Brain in Zurich.
For a up-to-date list of my publications check my profile on Google Scholar.
I am extremely lucky to (have) work(ed) with the following students: