PerTurbo
Robust and efficient analysis of single-cell perturbation studies — a scalable Bayesian analysis framework for large-scale Perturb-seq screens.
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.
Ph.D. in Biomedical Informatics
2020–2026 (Expected)
Harvard Medical School
A.B. in Molecular Biology, magna cum laude
2014–2018
Princeton University
Robust and efficient analysis of single-cell perturbation studies — a scalable Bayesian analysis framework for large-scale Perturb-seq screens.
A Nextflow pipeline for analyzing single-cell CRISPR screen data, developed within the IGVF consortium CRISPR Focus Group.
MEGA TRajectories of clONes — a computational framework for single-cell lineage tracing analysis using mitochondrial mutations.
In-house pipelines for calling copy number variants, structural variants, and single nucleotide variants from single-cell whole genome sequencing data.