Christopher Clark
Tacoma, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolChristopher Clark is a machine learning and NLP researcher completing a PhD at the University of Washington under Luke Zettlemoyer, with 13 years of engineering experience spanning research internships at Google and AI2 and production-focused roles contributing to open-source tools. His work centers on making models robust to spurious heuristics—e.g., preventing shortcuts like treating any sentence containing “not” as a contradiction—bridging theoretical insight with practical dataset and training improvements (he helped construct BoolQ, now part of SuperGLUE). He has a strong systems background from early industry internships (Facebook, WibiData) and backend contributions to notable AI2 repos such as pdffigures2 and document-qa, where he improved build systems, dependencies, and core training logic. Comfortable moving between code, data, and research, he combines philosophical training with rigorous CS and AI education (BS, MS, soon PhD), making him adept at dissecting problem assumptions as well as shipping reliable implementations.
13 years of coding experience
1 year of employment as a software developer
Bachelor of Arts - BA, Philosophy, Bachelor of Arts - BA, Philosophy at University of WashingtonMaster of Science - MS, Artificial Intelligence, Master of Science - MS, Artificial Intelligence at The University of Edinburgh
Github Skills (11)
dependency-management10
sbt10
tensorflow10
python10
build-automation10
scala10
machine-learning9
refactoring9
nlp9
software-packaging9
regular-expression8
Programming languages (5)
Github contributions (5)
allenai/document-qa
Aug 2017 - May 2018
Role in this project:
Back-end Developer
Back-end DeveloperContributions:78 commits, 4 PRs, 34 pushes in 9 months
Contributions summary:Christopher primarily focused on refactoring and renaming code, specifically changing instances of "runner" to "trainer" across multiple files. They also made minor fixes and adjustments to prediction layers, including modifications to aggregation strategies for loss calculations. These changes appear to be targeted at the core training and prediction logic within the document QA framework.
allenai/pdffigures2
May 2016 - Nov 2021
Given a scholarly PDF, extract figures, tables, captions, and section titles.
Role in this project:Backend Developer
Backend DeveloperContributions:22 commits, 8 PRs, 11 pushes in 5 years 7 months
Contributions summary:Christopher primarily focused on modifying the build process and dependencies of the project. They added Bintray and associated metadata to the build configuration, removed an allenai common dependency, and updated the project's dependencies. Furthermore, the user made minor adjustments to the codebase by removing a debug file and tweaking a regular expression in the code. They also made formatting improvements to the code.
pdffiguresextract-pdfcaptionsscholarly
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