Yoni has an interdisciplinary background with a formal education in neuroscience, biomedical science, economics, and artificial intelligence. With years of experience in operational and technical roles, he now wishes to bridge the gap between artificial intelligence, medical science, and clinical implementation of AI models. He obtained his master's degree in AI at the University of Amsterdam in 2020. His thesis titled "Predicting DNA Damage Repair Deficiencies directly from H&E Whole-Slide Images using Deep Learning" was supervised by dr Jonas Teuwen, dr Hugo Horlings and dr Efstratios Gavves.
He is now a PhD candidate under the HISTO-AI project (2020-2024), with the aim to develop deep learning methods to predict genomic and transcriptomic information directly from H&E WSIs. The larger goal of this project is to predict which patient can benefit from immune therapy or targeted therapy, directly from commonly available H&E WSIs.