Computational biologist working at the intersection of statistical machine learning, regulatory genomics, transcriptomics, and rare disease.

I am a principal investigator at Seattle Children's Research Institute and an associate professor at the University of Washington. My work focuses on probabilistic, mechanistic, and machine learning models that help quantify how genetic variation shapes gene regulation and human disease, especially in settings where the data are noisy, sparse, or otherwise hard to interpret.

Much of our lab software and public resources live under the PejLab organization. This profile is the best place to find my public-facing links and a few representative projects.

• Regulatory genomics and allele-specific analysis
• Multimodal transcriptomics and RNA phenotyping
• Rare disease genomics
• Statistical and mechanistic modeling for personalized medicine

• Lab website: pejlab.org
• Lab GitHub: github.com/PejLab
• Google Scholar: Publications
• Tools and data: pejlab.org/tools-and-data

• Pantry: A framework for generating diverse RNA phenotypes and integrating them with genetic analyses.
• aFCn: Haplotype-aware effect-size modeling for cis-regulatory variation.
• ANEVA: Quantifying gene dosage variation from allelic expression data.
• ANEVA-DOT: Dosage outlier detection for transcriptome-guided rare disease analysis.
• pejlab.github.io: Source for the lab website.

I use GitHub mostly for research software, methods papers, and public lab resources. Some older work and collaborations live in other organizations or under coauthors' repositories.