Christopher Clark

Tacoma, Washington, United States

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Summary

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Rockstar

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Top School

Christopher 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 Washington

Master of Science - MS, Artificial Intelligence, Master of Science - MS, Artificial Intelligence at The University of Edinburgh

Github Skills (12)

dependency-management10

sbt10

tensorflow10

python10

build-automation10

scala10

natural-language-processing9

machine-learning9

refactoring9

nlp9

software-packaging9

regular-expression8

Programming languages (5)

JavaC++ScalaJupyter NotebookPython

Github contributions (5)

allenai/pdffigures2

May 2016 - Nov 2021

Given a scholarly PDF, extract figures, tables, captions, and section titles.

Role in this project:

Backend Developer

Contributions: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.

pdf

allenai/document-qa

Aug 2017 - May 2018

Role in this project:

Back-end Developer

Contributions: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.

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