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Olivier Gevaert is an Associate Professor in the Department of Biomedical Data Science at Stanford University. His research focuses on biomedical data fusion and the development of machine learning methods for biomedical decision support employing multi-scale biomedical data. Gevaert has previously pioneered data fusion techniques using Bayesian kernel methods in studies related to breast and ovarian cancer. He has developed computational algorithms for identifying driver genes through multi-omics data and continues to work on innovative methods for bridging molecular data, such as omics, cellular data from pathology, and imaging data. His educational background includes a Ph.D. in Bioinformatics from the University of Leuven, Belgium, and dual Master’s degrees in Artificial Intelligence and Electrical Engineering/Computer Science from the University of Leuven and University College, Ghent, respectively. Gevaert has received multiple fellowships and has served in various professional organizations related to computational biology and medical informatics. He teaches several graduate-level courses in Biomedical Data Science and is involved in supervising multiple graduate research projects.
The Computer Science department emphasizes research potential. GRE General is currently optional but recommended for some tracks.