Statistical machine learning for regulatory genomics, transcriptomics, and rare disease

Pejman Mohammadi

PI, Associate Professor

Pejman is a computational biologist whose work sits at the intersection of statistical machine learning, probabilistic modeling, and genomics. His lab develops quantitative frameworks that formalize biochemical principles and regulatory mechanisms to improve inference from noisy, high-dimensional genomic data, with applications to regulatory variation, transcriptomics, rare disease, and personalized medicine.

Pejman joined the University of Washington and Seattle Children's Research Institute as an Associate Professor in 2023. Prior to that, he was on the faculty at Scripps Research in San Diego, first as an Assistant Professor and later as an Associate Professor. He holds a Ph.D. in Computational Biology from ETH Zurich and completed postdoctoral training at the New York Genome Center and Columbia University.

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

Yan Hao

Staff Scientist

Yan joined the lab as a staff scientist in 2024, focusing on the application of statistical machine learning models to analyze single-cell spatial transcriptomics and genomics within clinical trial datasets. Prior to this role, she concentrated her research on functional genomics, aiming to discover new drug targets for neurological disorders, immune diseases, and cancers. Yan holds a PhD in Cellular Biology from the University of Michigan and a Master's degree in Computer Science from Georgia Tech. Her postdoctoral work was conducted at AbbVie.

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 :)".

Katharine Chen

Postdoc

Katharine joined the lab as a postdoc in 2024. She is interested in applying and developing computational models to better understand the effects of eQTLs on their target genes using GTEx and other transcriptomic datasets. She holds a PhD in Molecular and Cellular Biology with a specialization in Data Science from the University of Washington, where she worked on developing massively parallel sequencing assays and CRISPR screens for identifying regulators of mRNA translation.

Tom Willis

Postdoc

Tom joined the lab as a postdoc in 2025. He is interested in the role of common variants in rare disease and methods which leverage cross-trait sharing of genetic architecture. During his PhD he studied the role of common variants in antibody deficiencies and was supervised by Chris Wallace at the MRC Biostatistics Unit, Cambridge. He is also interested in workflow automation and reproducibility.

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

Michael Yung

PhD Student

Michael joined the lab in 2024 and is a first-year graduate student. His project will involve the analysis of genetic determinants of regulatory variation in humans using previously collected and publicly available RNA sequencing data. He is interested in using statistical learning and high-dimensional data to solve complex problems in genetics and genomics. Before joining the lab, he was working with Bruce Weir on a research project related to the interpretation of Y-STR evidence, particularly on estimating population-specific values of theta for Y-STR Profiles. Michael holds a B.S. in Statistics and B.S. in informatics from University of Washington. LinkedIn

Shiyu Wan

PhD Student

Shiyu is interested in combining deep learning with statistical genomics to study regulatory variation and allele-specific signals. Prior to joining the lab, she developed deep learning and feature-importance methods for complex survival data. She holds a Bachelor of Medical Science in Public Health and a B.A. in Economics from Peking University, as well as an M.S. in Biostatistics from the University of North Carolina at Chapel Hill.