I am a Postdoc within the AI for Climate Impact team at IBM Research Europe. My work at IBM Research focuses on pretraining and finetuning foundations models for weather and earth observation in collaboration with NASA. I have a background in applied deep learning, having worked on a variety of topics during my PhD at KIT, my visiting research role at IBM Research, at ETH Zurich, and during several internships in industry.
At the moment, I am involved in projects with NASA, ESA, and within the EU HORIZON program. I am actively collaborating with researchers from institutions like MIT, Oxford, and ETH.
Before joining IBM as a postdoc, I served a PhD student at Karlsruhe Institute of Technology (KIT), focusing on applied deep learning, mainly in the context of data-centric AI. My thesis is entitled “Data-Centric Artificial Intelligence: Foundations and Methods for Deep Learning” and aims for providing scalable methods to systematically enhance data for deep learning. During my undergrad studies at KIT, I specialized in machine learning, statistics, and optimization and graduated with a bachelor’s and a master’s degree. During my master’s, I studied Information Systems and Computer Engineering at the Instituto Superior Técnico in Lisbon, Portugal and authored my master’s thesis at ETH Zurich at the Chair of Management Information Systems in the field of machine learning and optimization.
Throughout my studies, I have worked on real-world machine learning implementations (e.g., computer vision, natural language processing) during internships at Porsche motorsports, d-fine, and EnBW. For the first year of my Ph.D., I worked as an Affiliated Research Member at ETH Zurich to research emotion dynamics on social media. In October 2022, I joined IBM Research as a Visiting Researcher and since then have been working on geospatial and weather foundation models as well as unsupervised domain adaptation.
During the weekends, I love spending time in the mountains with my wife and our son, my family, and my friends.
