scDNA Analysis Pipeline
Pipelines for calling copy number variants, structural variants, and single nucleotide variants from single-cell whole genome sequencing data, developed during my time at the Dana-Farber Cancer Institute.
Logan is a Ph.D. candidate in Biomedical Informatics at Harvard Medical School building probabilistic machine learning systems that reason over large-scale biological data. His work in the Pinello Lab develops Bayesian & deep learning models — spanning representation learning, generative modeling, and causal inference — to turn high-throughput single-cell experiments into testable hypotheses about biological mechanism, with the broader goal of building computational systems that can reason under uncertainty and close the loop with experimental biology.
Outside of lab, he enjoys training for marathons (and the occasional IRONMAN triathlon) and bikepacking around Vermont.