Daniel Himmelstein
Chief Technology Officer at RadOverlay
Lebanon, New Hampshire, United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Himmelstein is a technology executive and data scientist who blends PhD-level bioinformatics with hands-on software engineering to build open-source tools and data platforms for biomedical discovery. As CTO and co-founder of RadOverlay and former Chief Data Scientist at Related Sciences, he has led teams to deliver production software—from the TI-RADS radiology report builder to the Facets drug-target prioritization platform—while stewarding large-scale data integration across ontologies like MeSH, EFO, and Ensembl. His research background includes pioneering metapath-based hetnet analyses for disease gene prioritization and creating Manubot to bring reproducible, collaborative publishing to science. An active open-source contributor, he has improved critical projects such as pandas and osmnx and automated CI/CD for Manubot, reflecting a rare mix of algorithmic insight, infrastructure practice, and reproducible research advocacy. Based in New Hampshire, he pairs academic rigor with product-focused delivery and an unusual early career in competitive policy debate that shaped his emphasis on clear scientific communication.
12 years of coding experience
7 years of employment as a software developer
High school diploma, High school diploma at Hanover High SchoolBiometry & Statistics Statistical Genomics, Biometry & Statistics Statistical Genomics at Cornell UniversityDoctor of Philosophy - PhD Biology & Medical Informatics, Doctor of Philosophy - PhD Biology & Medical Informatics at University of California, San Francisco
Stackoverflow
1,799reputation
868kreached
16answers
7questions
pandas
top-5%
numpy
top-5%
python
top-5%
Github Skills (46)
github-ci10
python10
snorkel10
data-science10
testing10
pandas10
machine-learning10
bash10
networkx10
geospatial10
cicd10
numpy10
mappings10
gis10
fileio10
Programming languages (32)
Github contributions (5)
manubot/rootstock
Jun 2017 - Nov 2022
Clone me to create your Manubot manuscript
Role in this project:
DevOps Engineer
DevOps EngineerContributions:81 reviews, 312 commits, 354 PRs in 5 years 5 months
Contributions summary:Daniel primarily focused on improving the build and deployment process of the repository. Their contributions included refactoring the CI/CD pipeline, specifically migrating from Travis CI to GitHub Actions. They updated build scripts to incorporate environment variables, manage dependencies, and streamline the build process using tools like Miniforge. Furthermore, the user addressed issues related to Docker usage and Bash scripting, enhancing the project's portability and maintainability.
citationdoimanubotpublishingmanuscript
greenelab/deep-review
Aug 2016 - Nov 2020
A collaboratively written review paper on deep learning, genomics, and precision medicine
Role in this project:Back-end Developer
Back-end DeveloperContributions:460 commits, 121 PRs, 654 pushes in 4 years 3 months
Contributions summary:Daniel's commits primarily focus on the development of a reference management system for a collaboratively written review paper. They implemented functionality to extract and compile references from text, along with the ability to auto-generate citations. Further commits included the transition from a notebook to a script, the implementation of automated reference management using `pandoc-manubot-cite`, and enabling the display of hyperlinks for references and DOI resolution and the use of OpenTimestamps for timestamping. The work involved using Python and related tools for text processing and automation.
manubotprecision-medicinedeep-learningneural-networksmachine-learning
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