Dan Levy

Dan Levy is an Associate Professor at Cold Spring Harbor Laboratory (CSHL), where he leads the Levy Laboratory and is a member of the Cancer Center. His research focuses on developing computational algorithms to identify disease-causing mutations from large genomic datasets.

• Ph.D. in Mathematics from University of California, Berkeley (2005)
• Postdoctoral training at Cold Spring Harbor Laboratory (2007-2009), University of Oxford (2006-2007), and University of California, Berkeley (2005-2006)
• Academic career at CSHL: Associate Professor (2012-present), Assistant Professor (2012), Senior Computer Scientist (2010-2012), Research Investigator (2009-2010)

Levy's research centers on identifying rare and unique mutations that play significant roles in diseases such as autism, congenital heart disease, and cancer. His group develops algorithms to analyze large, high-throughput datasets comprising thousands of nuclear families. Initially working with high-resolution CGH arrays, his team now primarily uses targeted sequence data. Key areas of algorithm development include identifying de novo mutations, detecting copy-number variants, and analyzing complex genomic rearrangements including insertions, deletions, inversions, and transpositions. Additional research projects involve single-cell RNA analysis, phylogenetic reconstruction from sparse datasets, and haplotype disentanglement from sperm and subgenomic sequence data.

His publication record demonstrates consistent contributions to computational genomics and disease genetics, with particular emphasis on autism genetics, cancer genomics, and methodological innovations. Recent work has expanded into applications for cancer diagnostics and minimal residual disease detection, reflecting the translational impact of his computational approaches.

• Active member of the CSHL Cancer Center
• Part of the Quantitative Biology and Genomics research groups at CSHL

Levy's work bridges computational mathematics with biological applications, developing sophisticated algorithms that enable researchers to identify previously undetectable genetic variations contributing to human disease.