Julia Salzman is an Associate Professor in the Department of
Biomedical Data Science, Biochemistry and Statistics (by Courtesy).
She received her A.B. in Mathematics from Princeton University
Magna Cum Laude and Ph.D. from Stanford University in the
Department of Statistics supervised by Dr. Persi Diaconis. As a
postdoctoral scholar in Dr. Patrick Brown’s lab, Dr. Salzman
developed statistical algorithms that led to the discovery of a
ubiquitous expression of circular RNA missed by other computational and experimental approaches for
decades. Her research spans the interface of statistical methodology and genomics aiming to use data
driven experiments to uncover organizing principles of biological regulation, historically focused on
RNA processing. Recently her group has introduced a new approach to sequencing analysis called
SPLASH that performs inference on raw sequencing data, bypassing genome alignment. This approach
is providing new insights into genome regulation in several biological domains.
Julia Salzman is an Associate Professor in the Department of
Biomedical Data Science, Biochemistry and Statistics (by Courtesy).
She received her A.B. in Mathematics from Princeton University
Magna Cum Laude and Ph.D. from Stanford University in the
Department of Statistics supervised by Dr. Persi Diaconis. As a
postdoctoral scholar in Dr. Patrick Brown’s lab, Dr. Salzman
developed statistical algorithms that led to the discovery of a
ubiquitous expression of circular RNA missed by other computational and experimental approaches for
decades. Her research spans the interface of statistical methodology and genomics aiming to use data
driven experiments to uncover organizing principles of biological regulation, historically focused on
RNA processing. Recently her group has introduced a new approach to sequencing analysis called
SPLASH that performs inference on raw sequencing data, bypassing genome alignment. This approach
is providing new insights into genome regulation in several biological domains.
Julia Salzman is an Associate Professor in the Department of
Biomedical Data Science, Biochemistry and Statistics (by Courtesy).
She received her A.B. in Mathematics from Princeton University
Magna Cum Laude and Ph.D. from Stanford University in the
Department of Statistics supervised by Dr. Persi Diaconis. As a
postdoctoral scholar in Dr. Patrick Brown’s lab, Dr. Salzman
developed statistical algorithms that led to the discovery of a
ubiquitous expression of circular RNA missed by other computational and experimental approaches for
decades. Her research spans the interface of statistical methodology and genomics aiming to use data
driven experiments to uncover organizing principles of biological regulation, historically focused on
RNA processing. Recently her group has introduced a new approach to sequencing analysis called
SPLASH that performs inference on raw sequencing data, bypassing genome alignment. This approach
is providing new insights into genome regulation in several biological domains.