PhD
Chair of Artificial Intelligence and Machine Learning at LMU
Institute of Informatics
Akademiestr. 7
80799 München
Biosketch
Yusuf obtained his Master’s degree in Statistics at the University of Munich (LMU). His studies focused on the theoretical foundations of statistics, particularly in the representation and quantification of uncertainty.
As a student assistant, he gained experience in academic teaching and research early on. After a short stay abroad in France, he joined the Chair of Artificial Intelligence and Machine Learning. Since September, he is a relAI member.
relAI Research
Uncertainty Representation and Quantification in Machine Learning
My research primarily focuses on uncertainty quantification in machine learning (ML), with a strong emphasis on distinguishing and interpreting different types of uncertainty, namely total, aleatoric, and epistemic. A recurring theme in my work is the (critical) evaluation of existing uncertainty measures, identifying their limitations, and proposing more suitable alternatives grounded in principled frameworks. I aim to provide a deeper understanding of how uncertainty is represented, quantified, and interpreted, while also raising awareness of potential flaws and pitfalls in widely adopted methods.