School of Medicine
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Maya M. Kasowski
Assistant Professor of Medicine (Sean N Parker Center for Allergy and Asthma Research) of Pathology and, by courtesy, of Genetics
Bio I am a clinical pathologist and assistant professor in the Departments of Medicine, Pathology, and Genetics (by courtesy) at Stanford. I completed my MD-PhD training at Yale University and my residency training and a post-doctoral fellowship in the Department of Genetics at Stanford University. My experiences as a clinical pathologist and genome scientist have made me passionate about applying cutting-edge technologies to primary patient specimens in order to characterize disease pathologies at the molecular level. The core focus of my lab is to study the mechanisms by which genetic variants influence the risk of disease through effects on intermediate molecular phenotypes.
Mark A. Kay, M.D., Ph.D.
Dennis Farrey Family Professor of Pediatrics, and Professor of Genetics
Current Research and Scholarly Interests Mark A. Kay, M.D., Ph.D. Director of the Program in Human Gene Therapy and Professor in the Departments of Pediatrics and Genetics. Respected worldwide for his work in gene therapy for hemophilia, Dr. Kay and his laboratory focus on establishing the scientific principles and developing the technologies needed for achieving persistent and therapeutic levels of gene expression in vivo. The major disease models are hemophilia, hepatitis C, and hepatitis B viral infections.
Violetta L. Horton Professor and Professor of Microbiology and Immunology
Current Research and Scholarly Interests The biochemistry of RNA-dependent RNA polymerase function, the cell biology of the membrane rearrangements induced by positive-strand RNA virus infection of human cells, and the genetics of RNA viruses, which, with their high error rates, live at the brink of error catastrophe, are investigated in the Kirkegaard laboratory.
Assistant Professor of Genetics and of Computer Science
Current Research and Scholarly Interests We develop statistical and machine learning frameworks to learn predictive, dynamic and causal models of gene regulation from heterogeneous functional genomics data.