Jonathan Rees
Research Affiliate
Salem, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolJonathan Rees is a research-oriented software engineer and data architect with over three decades of technical depth and eight years focused on biodiversity informatics and taxonomy. He bridges biology and computation—designing data formats, curation workflows, and federated publishing systems—while assessing how taxonomic change ripples through datasets at Arizona State University. Jonathan’s background spans probabilistic language interpreters at MIT, large-scale semantic web and linked-data projects at Creative Commons and W3C, and leadership on the Open Tree of Life synthesis at Duke. He combines rigorous academic training (PhD, MIT) with a pragmatic “don’t do anything someone else can do” ethos, favoring interoperable, reusable infrastructure that amplifies biological research. An unusual strength is his track record of building federated, high-scale knowledge systems (including half-billion triple RDF stores) that make messy scientific data actionable.
9 years of coding experience
20 years of employment as a software developer
PhD, Electrical Engineering and Computer Science, PhD, Electrical Engineering and Computer Science at Massachusetts Institute of TechnologyBS, Computer Science, Mathematics, BS, Computer Science, Mathematics at Yale University
English, French
Github Skills (12)
database-table9
bayesian8
probabilistic-programming8
embedded-language8
data-science7
clojure7
databases7
machine-learning7
metaprogramming7
sql-database6
sqlite4
python3
Programming languages (3)
Github contributions (5)
probcomp/metaprob
Dec 2017 - Aug 2018
An embedded language for probabilistic programming and meta-programming.
Contributions:10 commits, 6 PRs, 21 pushes in 7 months
embedded-languagemetaprogrammingprobabilistic-programmingdata-scienceclojure
probcomp/probcomp-stack
Jul 2017 - Aug 2017
MIT Probabilistic Computing Project software stack
Contributions:1 PR, 5 pushes, 1 branch in 1 month
pythonsoftware-stackmachine-learningprobabilisticcomputing
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