Stanford University
Showing 261-280 of 368 Results
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Michelle Ozaki
Postdoctoral Scholar, Genetics
Current Research and Scholarly InterestsResearch interests include tumor microenvironment interactions, stromal and tumor cell interactions, and how stromal cells impact metastasis.
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John R. Pringle
Professor of Genetics, Emeritus
Current Research and Scholarly InterestsMuch of our research exploits the power of yeast as an experimentally tractable model eukaryote to investigate fundamental problems in cell and developmental biology such as the mechanisms of cell polarization and cytokinesis. In another project, we are developing the small sea anemone Aiptasia as a model system for study of the molecular and cellular biology of dinoflagellate-cnidarian symbiosis, which is critical for the survival of most corals but still very poorly understood.
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Jonathan Pritchard
Bing Professor of Population Studies, Professor of Genetics and Biology
On Leave from 01/01/2026 To 06/15/2026Current Research and Scholarly InterestsWe are interested in a broad range of problems at the interface of genomics and evolutionary biology. One current focus of the lab is in understanding how genetic variation impacts gene regulation and complex traits. We also have long-term interests in using genetic data to learn about population structure, history and adaptation, especially in humans.
FOR UP-TO-DATE DETAILS ON MY LAB AND RESEARCH, PLEASE SEE: http://pritchardlab.stanford.edu -
Xiaojie Qiu
Assistant Professor of Genetics and, by courtesy, of Computer Science
Current Research and Scholarly InterestsAt the Qiu Lab, our mission is to unravel and predict the intricacies of gene regulatory networks and cell-cell interactions pivotal in mammalian cell fate transitions over time and space, with a special emphasis on heart evolution, development, and disease. We are a dynamic and interdisciplinary team, harnessing the latest advancements in machine learning as well as single-cell and spatial genomics by integrating the predictive power of systems biology with the scalability of machine learning,
