Matthew Lincoln is a senior software engineer and data engineering leader in San Francisco with 13 years of experience building systems that transform unstructured documents into structured knowledge and ML-ready datasets. He combines hands-on engineering and team leadership from production data pipelines to research-driven projects, most recently leading engineering at ITHAKA and now at Prolific. His background in art history and digital humanities informs a unique focus on cultural heritage technology—he’s contributed tidy methods for randomForest models in the widely used tidymodels/broom ecosystem and improved accessibility and content workflows for the Programming Historian Jekyll site. Comfortable bridging research and production, he has served as a Co-PI and data consultant on projects converting legacy scholarship into Linked Open Data. Colleagues rely on him to translate domain complexity into reliable, auditable data tooling that supports scholarship and scalable services.
13 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Art History, Doctor of Philosophy (Ph.D.), Art History at University of Maryland
B.A., Art History, B.A., Art History at Williams College
Jekyll-based static site for The Programming Historian
Role in this project:
Full-stack Developer
Contributions:950 commits, 432 PRs, 848 pushes in 5 years 8 months
Contributions summary:Matthew primarily contributed to the website's front-end and structure, making changes to the layout, navigation, and styling. The commits reflect a focus on enhancing the user experience and improving accessibility through changes to the menu, header, and footer. Furthermore, the user implemented features related to lesson citations and blog post pagination, indicating involvement in content presentation and organization.
Convert statistical analysis objects from R into tidy format
Role in this project:
Data Scientist
Contributions:34 commits, 2 PRs, 5 comments in 1 year 1 month
Contributions summary:Matthew contributed to the `broom` package by implementing tidiers for randomForest models, converting the output into a tidy format. They added functions for tidying, augmenting, and glancing at randomForest models, specifically for classification trees. These contributions include basic implementations for classification and regression randomForest models.
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