Dr. Marylyn D. Ritchie is the Edward Rose, MD and Elizabeth Kirk Rose, MD Professor of Genetics and Director of the Institute for Biomedical Informatics at the University of Pennsylvania School of Medicine. She is also Co-Director of the Penn Medicine BioBank and Vice President of Research Informatics in the University of Pennsylvania Health System. Dr. Ritchie is an expert in translational bioinformatics, with a focus on developing, applying, and disseminating algorithms, methods, and tools integrating electronic health records (EHR) with genomics. Dr. Ritchie has over 20 years of experience in translational bioinformatics and has authored over 375 publications. Dr. Ritchie was appointed as a Fellow of the American College of Medical Informatics (ACMI) in 2020. Dr. Ritchie was elected as a member of the National Academy of Medicine in 2021; she is being recognized “for paradigm-changing research demonstrating the utility of electronic health records for identifying clinical diseases or phenotypes that can be integrated with genomic data from biobanks for genomic medicine discovery and implementation science.” Dr. Ritchie is also the host of The CALM Podcast: Combining Academia and Life with Marylyn.

The mission of the Ritchie Lab is to improve our understanding of the underlying architecture of common, complex diseases. We develop and apply a breadth of translational bioinformatics approaches exploring the genome, the phenome, and the exposome. The approaches we take involve the development and application of new statistical, computational, machine learning, and AI methods with a focus on embracing complexity to uncover relationships between multi-omics data, clinical data (mostly from electronic health records), environmental exposures, and social determinants of health. These meta-dimensional approaches hold the promise of providing a more comprehensive view of genetic, genomic, and phenotypic information.

All of these tools and methodologies that the Ritchie Lab develops focus on Big Data applications and emphasize improvements in visual analytics as we embrace the new horizons of genomic and phenomic information.