Brian Heseung Kim

@brhkim · User

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Data Science Leader & Education Policy Researcher // Building rigorous, open-source AI infrastructure for the common good

Open Augments, LLCBoston, MA29 followers16 repositories

Repositories

brhkim/public_demos

Public demos, explainers, and otherwise helpful things to share with others

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brhkim/fortran_skill_test

Basic pass at having Fable produce a Fortran skill with explicit references, syntax, etc. for coding assistants. I've never used Fortran, just for demo purposes!

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brhkim/daaf_fork

DAAF, the Data Analyst Augmentation Framework: An open-source, extensible workflow for Claude Code that allows skilled researchers to rapidly scale their expertise and accelerate data analysis by as much as 5-10x -- without sacrificing the transparency, rigor, or reproducibility demanded by our core scientific principles.

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brhkim/teacher_recommendations_nlp

Replication Code Archive for "What's in a Letter? Using Natural Language Processing to Investigate Systematic Differences in Teacher Letters of Recommendation"

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brhkim/mapping-education-deserts

This is a companion repository of code for the paper The Last Mile in School Access: Mapping Education Deserts in Developing Countries, by Daniel Rodriguez-Segura and Brian Heseung Kim. The article is available open-source via Development Engineering here: https://doi.org/10.1016/j.deveng.2021.100064

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brhkim/extracurricular_trends_common_app

Replication Code Archive and Online Appendix for "Inequality beyond Standardized Tests: Trends in Extracurricular Activity Reporting in College Applications Across Race and Class" research paper by Park et al.

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brhkim/n2fl_nlp_public

Public repository for the paper "Gauging Engagement: Measuring Student Response to a Large-Scale College Advising Field Experiment"

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brhkim/teacher_spending_analysis

Replication code for the report "Supporting Students at Any Cost? Examining the Dynamics of Teacher Out-of-Pocket Spending, Student Demographics, and Teacher Autonomy"

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brhkim/parttree

Simple package for plotting decision tree partitions in R

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