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Home » Speakers » Katie Pollard
Katie Pollard

Katie Pollard

University of California, San Francisco

Senior Investigator and Director of the Gladstone Institute of Data Science and Biotechnology at the Gladstone Institutes; Professor in the Department of Epidemiology & Biostatistics, Institute for Human Genetics, Quantitative Biology Institute, and Institute for Computational Health Sciences at the University of California, San Francisco. Gladstone Institute

Dr. Katherine S. Pollard is Director of the Gladstone Institute of Data Science & Biotechnology and Professor in the Department of Epidemiology & Biostatistics, Institute for Human Genetics, Quantitative Biology Institute, and Institute for Computational Health Sciences at the University of California, San Francisco. Previously, Dr. Pollard was an assistant professor in the University of California, Davis Genome Center and Department of Statistics. Dr. Pollard earned her PhD in Biostatistics from the University of California, Berkeley and was a Bioinformatics postdoctoral fellow at the University of California, Santa Cruz.

Katherine Pollard was awarded the Thomas J. Watson Fellowship in 1995 and the Sloan Research Fellowship in 2008. The UC Berkeley School of Public Health recognized her as Alumna of the Year in 2013 and as one of its 75 most influential alums in 2018. The San Francisco Business Times honored her in 2018 as one of its Women Who Lead in the Life Sciences. She is a member of the California Academy of Sciences and a Chan-Zuckerberg Biohub Investigator.

The Pollard lab develops bioinformatics methods for comparative analysis of massive biological datasets, with a focus on genomics and other transformative technologies. The lab’s mission is to enable statistically rigorous, quantitative comparisons across species, developmental stages, and conditions. A major emphasis is creating open source bioinformatics software. Pollard and her team developed industry standard tools for gene expression analysis, detecting evolutionary conservation and acceleration, and quantifying abundances of microbial genes from metagenomes.

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This material is based upon work supported by the National Science Foundation and the National Institute of General Medical Sciences under Grant No. MCB-1052331. Any opinion, finding, conclusion, or recommendation expressed in these videos are solely those of the speaker and do not necessarily represent the views of iBiology, the National Science Foundation, the National Institutes of Health, or other iBiology funders.

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