Pejman Mohammadi

PI, Associate Professor

Pejman is a computational biologist with a background primarily in statistical machine learning and Bayesian data modeling. His current research interests span over practical and theoretical issues that arise in genomics and personalized medicine, focusing on quantitative analysis of regulatory variation in the genome and its application to rare diseases.

Pejman jointed UW as an Associate Professor in 2023. Prior to that he was at Scripps Research in San Diego as a tenure track Assistant Professor in 2018, later as an Associate Professor from 2021. He holds a Ph.D. in Computational Biology from ETH Zurich and carried out postdoctoral work at the NY Genome Center and Columbia University from 2015 to 2017.

pejmanm@uw.edu

Amy Crowson

Lab Administrative Coordinator

amy.crowson@seattlechildrens.org

Daniel Munro

Staff Scientist

Daniel joined the lab in 2020 and has a joint position in the Palmer Lab at UC San Diego. He is developing methods to identify regulatory variation in outbred rats with applications in psychiatry, as well as tools for extracting biological features from RNA-Seq. He did his PhD in Quantitative and Computational Biology at Princeton University with Mona Singh. Personal website

Robert Vogel

Staff Scientist

Robert joined the lab as a postdoc in 2021. He is interested in developing and applying computational methods for uncovering novel biological insights from genomic and transcriptomic data. Prior to joining the lab, he worked on developing and applying nonlinear dynamic and statistical models for understanding cellular response variability observed in single-cell measurements and in binary classification tasks. He holds a Ph.D. in Physiology, Biophysics, and Systems Biology from the Weill Cornell Graduate School of Medical Sciences and completed postdoctoral work at IBM Research and the Icahn School of Medicine at Mount Sinai.

Mehdi Esmaeili-Fard

Postdoc

Mehdi joined the lab in 2023. He is a computational and quantitative biologist and applies statistical methods to detect genomic variations underlying disease/traits. Mehdi also has wide experience in whole-genome prediction using statistical approaches. At the PejLab, Mehdi's work focuses on applying bioinformatics and computational methods to conduct GWAS and eQTL/single-cell eQTL analyses to identify genes and pathways in the cochlear-vestibular system related to balance in older adults. Mehdi is interested in multi-omics analyses using different layers of genomic data that can lead to a better connection between genotype and phenotype and a better systematic understanding of the information flow across different omics layers. "By the way, our ultimate goal is improving human health :)".

Kaushik Ram Ganapathy

PhD Student

Kaushik joined the lab in 2021 and is a second-year graduate student. His thesis project involves developing methods for rare variant outlier detection. He is interested in using data science to help improve human health. Before joining Scripps, he co-founded and led GeoACT, a project to model COVID-19 spread in schools working at the San Diego Supercomputer Center with Dr. Ilya Zaslavsky. Kaushik holds a B.S. in Data Science from the Halicioglu Data Science Institute, UC San Diego. Google Scholar | LinkedIn

Sarah Silverstein

MD-PhD Student

Sarah is a fourth-year medical student at Rutgers New Jersey Medical School and current PhD student with the Rutgers-NIH graduate partnership program. She joined the lab of Carsten G Bönnemann MD at the NNDCS/NIH and David Adams MD PhD at the Undiagnosed Diseased Program/NIH in 2020. Her thesis project focuses on using whole transcriptomic sequencing to improve molecular diagnosis in genome and exome negative rare disease patients. Upon graduation, she plans on pursuing clinical training in pediatric neurology with a focus in neurogenetics.