Leonardo Collado Torres

Leonardo Collado Torres is an Assistant Professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health and leads the R/Bioconductor-powered Team Data Science at the Lieber Institute for Brain Development (LIBD). His interdisciplinary research bridges biostatistics, bioinformatics, and neuroscience to study psychiatric disorders through high-throughput genomic technologies.

• PhD, Johns Hopkins Bloomberg School of Public Health, 2016
• BS, National Autonomous University of Mexico (UNAM), 2009

Dr. Collado Torres specializes in the analysis of RNA-seq, single-cell/nucleus RNA-seq, and spatial transcriptomics data, with a focus on gene expression across multiple biological scales. His work emphasizes reproducibility, open science, and the development of accessible computational tools. He is a strong advocate for data science training and mentorship, organizing initiatives such as the LIBD rstats club and contributing to open-source R/Bioconductor packages like derfinder, recount, and spatialLIBD.

His recent publications highlight advancements in spatial gene expression mapping in the human brain and tools for reproducible transcriptomic analysis. These works span disciplines including neuroscience, genomics, and software development, with subfields such as spatial transcriptomics, psychiatric disorder mechanisms, and open-source bioinformatics tooling.

Scientific honors include the CONACyT scholarship and service on the Bioconductor Community Advisory Board and rOpenSci’s Statistical Software Peer Review advisory board.

• Principal Investigator, R/Bioconductor-powered Team Data Science, LIBD
• Co-founder, LIBD rstats club
• Co-founder, Community of Bioinformatics Software Developers (CDSB), Latin America
• Active contributor to R/Bioconductor open-source projects

Dr. Collado Torres fosters a collaborative and inclusive research environment, offering data science guidance sessions and comprehensive onboarding resources for team members. His leadership promotes best practices in coding, reproducibility, and scientific communication.