Yves Moreau is a full professor at KU Leuven. He does research on computational methods for systems biology to understand how variation in a personal genome leads to variation in risk and severity of genetic disease through differential perturbation of molecular networks. At the application level, he focuses on diagnosis and disease gene discovery in congenital genetic disorders. Methodologically, he focuses on kernel methods and probabilistic graphical models for fusion of multiple, heterogeneous sources of data to prioritize candidate disease genes. He also focuses on the computational infrastructure necessary to bring new technologies (array comparative genomic hybridization and next-generation sequencing) to routine clinical application in medical genetics. He coordinates SymBioSys "from variome to phenome", a large interfaculty effort within the university to leverage next-generation sequencing and data integration to understand molecular events flowing from genomic variation to phenotype variation in human genetic disorders.
Adam Arany (Google Scholar) completed his computer science MSc degree at the Technical University of Budapest, and acquired a PhD degree in pharmaceutical sciences at the Semmelweis University, Budapest. He is currently research expert at KU Leuven specializing in deep learning, Bayesian modeling, causal inference and their applications for drug discovery among others. He participated in multiple academic-industry collaborations with different pharmaceutical companies aiming to tackle real world challenges of drug discovery with machine learning tools. His publication track record also include technical publications on conferences like NEurIPS and ICLR. He is regular reviewer of prestigious ML conferences.