Yusuf Berk Oruc is a doctoral student for machine learning in oncology at the German Cancer Consortium (DKTK)/German Cancer Research Center (DKFZ). His research focuses on the interpretable probabilistic ML models for multimodal data integration. Specifically, he is applying these techniques to a project on novel mRNA technology applications for colorectal and pancreatic cancer. He received his master’s degree from Georg-August University of Göttingen. During his masters, his research focused on the development of a particle picking models for Cryo-ET that required no manually labeled data. Instead, it derived all its supervised signals from simulated data and unsupervised signals from real data. This strategy had the capacity to reduce the annotation burden drastically, enabling the analysis of unlabeled cryo-ET data with deep learning techniques that perform on par with fully supervised models.