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Kimberly Smith

Director, Molecular Biology

Bio:

Kimberly Smith joined the Allen Institute in October 2003 as the first bench scientist, bringing with her over 7 years of experience overseeing large scale genomic and transcriptomics projects. Since joining the Institute, Kim’s focus has been on influencing, producing, and managing many of the most well-known Brain-map products. In particular, she has managed production groups, methods development, and data handling for six research atlases including the signature mouse brain atlas and mouse spinal cord atlas. Kim currently serves as the Director of Molecular Biology, directly managing the Single Cell RNASeq Core. The Molecular Biology department also includes the Histology, Genotyping, and Bioinformatics Core teams responsible for providing core resource services to all of Brain Science.

Kim’s first experiences with large scale genomics project management was working on Chromosome 7 of the Human Genome Project, the Pseudomonas Genome Project, and the Rice Genome Project, all while at the University of Washington. She then went on to manage and develop the lab operations of the Center for Expression Arrays at the University of Washington, branching out into the new Microarray technology at the time. After a few years at a new biotechnology venture that did not succeed, Kim welcomed the opportunity to start at the ground floor of the Allen Institute for Brain Science, employing her experience both at the bench and at establishing new production pipelines.

Kim received a B.S. in Cell and Molecular Biology from the University of Washington.

Research Focus:

As the first bench scientist hired at Allen Institute for Brain Science, Kim has enjoyed being a part of the growth and development of the Institute since 2003. She is passionate about generating high quality datasets that have high impact on scientific understanding of our world. She enjoys developing new processes and production pipelines, focusing on data integrity and data quality, as the backbone to generating these datasets.

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