Develop methods and tools for precision medicine
Manuel Rivas is an accomplished academic and researcher currently serving as an Assistant Professor of Biomedical Data Science at Stanford University. His journey began with a Bachelor of Science in Mathematics from the Massachusetts Institute of Technology (MIT). He then pursued a Doctor of Philosophy in Human Genetics at the University of Oxford, where he was a Clarendon Scholar, deepening his expertise in genetic research. Following his doctoral studies, Rivas enhanced his skills through post graduate training at the Broad Institute in Cambridge, Massachusetts. There, he led the Helmsley Inflammatory Bowel Disease Exome Sequencing Program, investigating genetic factors contributing to ulcerative colitis and Crohn’s disease, which solidified his reputation in genetic epidemiology and computational biology.
At Stanford, Rivas heads the Rivas Lab, focusing on population analytics using genomic and phenotypic data. His research emphasizes developing statistical models and computational tools to analyze vast datasets, generating therapeutic hypotheses for diseases, and advancing integrated learning healthcare systems. His work integrates diverse data types—genetics, imaging, and health records - to address global health challenges.
Currently, Rivas is also engaged at Stanford’s d.school, where he teaches a project-based course on "Generative AI for Healthcare" (Design 266 / BIODS 295). Offered in Spring 2024, the class explores how generative AI can transform healthcare, from diagnostics to personalized treatments. Students use cutting-edge AI models, applying human-centered design principles—empathy, prototyping, and iteration—to real-world healthcare challenges. Using datasets like population biobanks, they train models for applications such as medical image synthesis and variant effect prediction, bridging technology and patient care innovatively.