Patrick Schultz
Principal Software Engineer at Broad Institute of MIT and Harvard
Somerville, Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolPatrick Schultz is a Principal Software Engineer with 11 years of experience building cloud-native, distributed data systems at the Broad Institute, where he has advanced through engineering ranks since 2017. He contributes to Hail, a prominent open-source project for large-scale genomic dataframes and batch computing, adding algorithms, optimized hash functions, and interval-based methods that improve distributed performance. With a PhD in mathematics and postdoctoral research in category theory at MIT, he brings deep theoretical insight to practical engineering problems and helped commercialize a categorical database model. Patrick blends research-grade formalism with hands-on backend and data engineering, focusing on scalable, reliable tools for genomics. Based in Somerville, MA, he’s comfortable navigating both academic collaborations (including NASA-related formal frameworks) and production engineering teams. An understated strength is his ability to translate abstract mathematical structures into efficient, testable software primitives used in large-scale distributed computation.
11 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Mathematics, Doctor of Philosophy (Ph.D.) Mathematics at University of OregonUniversity of California Santa Cruz
Github Skills (10)
algorithm10
data-structures10
algorithms10
hashing-algorithm10
data-structure10
data-engineering10
scala10
distributed-computing9
python6
mlr4
Programming languages (5)
Github contributions (5)
hail-is/hail
May 2017 - Jan 2023
Cloud-native genomic dataframes and batch computing
Role in this project:
Back-end Developer & Data Engineer
Back-end Developer & Data EngineerContributions:1 release, 1067 reviews, 259 commits in 5 years 9 months
Contributions summary:Patrick focused on enhancing the Hail project by contributing new algorithms and data structures for processing genomic data. The user implemented and optimized various hash functions and methods for interval-based computations, including approximate CDF calculations. The user also improved the Table data structure, and introduced methods to streamline operations, particularly during distributed computation tasks.
scalabledata-analysispythongeneticshail
patrick-schultz/hail
Apr 2017 - Jan 2025
Scalable genetic data analysis.
Contributions:1 review, 6 PRs, 1222 pushes in 7 years 10 months
scalabledata-analysispythongenetic-datagenetic
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