Patrick Hall
Chief AI Officer, GW School Of Business at HallResearch.ai
Washington, District of Columbia, United States
Summary
🤩
Rockstar🎓
Top SchoolPatrick Hall is a Chief AI Officer and educator with 11 years of experience helping organizations manage ML and AI risk through consulting, research, and teaching at The George Washington University and HallResearch.ai. He blends product and research experience from H2O.ai and SAS—where he led explainable AI, model debugging, and earned patents related to clustering—with hands-on work in model interpretability, fairness, and governance. Patrick contributes practical open-source resources (including a popular interpretability knowledge base and GWU course materials) and supports national standards through research for NIST’s AI Risk Management Framework. Based in Washington, D.C., he advises industry and academic projects, runs niche high-stakes model audits, and volunteers on the AI Incident Database board, reflecting a rare mix of technical depth, policy engagement, and community stewardship.
11 years of coding experience
9 years of employment as a software developer
Master of Science (M.S.) Analytics, Master of Science (M.S.) Analytics at North Carolina State UniversityB.A. Mathematics, B.A. Mathematics at The University of North Carolina at Chapel HillComputational Chemistry, Computational Chemistry at University of Illinois Urbana-Champaign
Stackoverflow
71reputation
4kreached
2answers
0questions
Github Skills (18)
xgboost10
interpretation10
python10
sas10
data-science10
data-manipulation10
machine-learning10
h2o10
decision-tree10
data-analysis10
neural-network9
deep-learning9
clustering9
kmeans8
pca8
Programming languages (5)
Github contributions (5)
jphall663/GWU_data_mining
Jan 2016 - Nov 2020
Materials for GWU DNSC 6279 and DNSC 6290.
Role in this project:
Data Scientist
Data ScientistContributions:441 commits, 5 PRs, 420 pushes in 4 years 11 months
Contributions summary:Patrick's commits primarily involve implementing SAS code for basic data manipulation. These include subsetting and modifying columns, subsetting rows, and performing data analysis techniques such as frequency analysis and univariate analysis, as well as exporting and importing data using PROC EXPORT and PROC IMPORT. The user seems to be working with a SAS data set to understand and work with the data. The code suggests a focus on learning data manipulation and analysis tasks.
pythonh2oimage-recognitiondata-sciencesas
jphall663/interpretable_machine_learning_with_python
Sep 2017 - Feb 2021
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Role in this project:Data Scientist
Data ScientistContributions:150 commits, 12 PRs, 139 pushes in 3 years 5 months
Contributions summary:Patrick's commits primarily focus on building and exploring machine learning models. Specifically, they worked on an XGBoost model, partial dependence, ICE, and Shapley explanations for the purpose of model debugging. The user also worked to calculate Adverse Impact Ratios (AIRs) to better understand the performance of the model. This indicates an interest in machine learning interpretability and fairness.
ml-modelspythongradient-boosting-machinefairness-mlxai
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