Samuel Hoffman
Research Software Engineer at IBM
New York City Metropolitan Area United States
Summary
🤩
Rockstar🎓
Top SchoolSamuel Hoffman is a Research Software Engineer based in the New York City area with a decade of experience building and hardening machine learning and computer vision systems. At IBM since 2017 he translates research ideas into reliable software, drawing on early hands-on work in deep convolutional models for product recognition and real-time vision tools. He holds a dual-degree from Cornell Engineering with a 4.0 GPA and a background that spans academic research labs and production-focused internships in finance and embedded vision. Samuel contributes to open-source fairness tooling—improving developer ergonomics and error handling in the widely used AIF360 project—indicating attention to usability and robust data loading. He combines rigorous engineering discipline with practical product sensibilities, often improving debugging and documentation to make complex ML workflows more accessible. Colleagues can expect a researcher-minded engineer who values clear errors, reproducible code, and experiments that scale to production.
9 years of coding experience
1 year of employment as a software developer
High School, High School at Thomas Jefferson High School for Science and TechnologyBachelor’s Degree, Computer Science and Mechanical Engineering, Cumulative GPA 4.0, Bachelor’s Degree, Computer Science and Mechanical Engineering, Cumulative GPA 4.0 at Cornell University College of Engineering
English, Spanish
Github Skills (6)
machine-learning10
f10
n10
python10
data-science10
documentation9
Programming languages (3)
Github contributions (5)
Trusted-AI/AIF360
Sep 2018 - Dec 2022
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Role in this project:
Data Scientist
Data ScientistContributions:12 releases, 71 reviews, 223 commits in 4 years 3 months
Contributions summary:Samuel's commits focused on fixing docstring indentations across various Python files within the project. They also removed references to outdated documentation and files. In addition, the user's contributions included adding detailed error messages for dataset placement, demonstrating a focus on improving user experience and debugging workflows within the project related to the dataset loading processes.
fairness-mlpythondiscriminationbias-reductionfairness-testing
hoffmansc/AIF360
Sep 2022 - Sep 2023
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
Contributions:2 pushes, 2 branches in 1 year
fairness-mlexplanationsmachine-learningbiasdatasets
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