Welcome

I am a Machine Learning Engineer at Apple, previously post-doctoral researcher at the Chair for Applied Software Engineering at the Technical University of Munich. I have over 15 years experience conducting research in data-centric machine learning.

Research Interests

My current research area is on efficient data annotation. I work on methods to 1) save annotation costs, e.g. using models in the annotation loop, semi-supervised learning or active learning and 2) improve annotation quality, e.g. with consensus and ensemble methods to aggregate annotations from multiple graders or more advanced methods, such as LLM as a judge. Previously, I worked on activity recognition methods for devices with limited compute capabilities such as wearable and mobile devices.

Brief Bio

I obained a Bachelor’s degree in computer science at the Pompeu Fabra University with honors and an award to the highest grades of the 2009-promotion. I then moved to Germany where I studied Computational Science and Engineering at the Technical University of Munich with the financial support of a fellowship from the Fundación Caja Madrid. I obtained my doctoral degree in software engineering with a focus on machine learning for wearable devices in 2016 with suma cum laude. Driven by different exciting research ideas I could not finish during my Ph.D., I did a four year post-doc and obtained a Habilitation (highest degree attainable in Germany). During my professional career, I obtained different awards and published my research at top scientific conferences. I joined Apple in 2020 and I am still loving working here.

Find me on…

You can find me on Google Scholar, LinkedIn and Github.