Stephen Bach
Postdoctoral Scholar at Stanford University
Providence, Rhode Island, United States
Summary
🤩
Rockstar🎓
Top SchoolStephen Bach is a postdoctoral scholar and software engineer with 14 years of experience bridging academic research and production software engineering. Trained with a PhD in Computer Science from the University of Maryland and a BS in CS and Math from Georgetown, he combines rigorous research instincts with practical system-building skills. At Stanford he focuses on projects that require careful data modeling and scalable back-end design, exemplified by his ORM and database backend contributions to Snorkel’s core parsing data structures. He also contributes full-stack improvements to NLP tooling like PromptSource, enhancing template creation UIs and dataset workflows for the broader prompting community. Based in Providence, RI, he brings a knack for turning complex information-extraction requirements into maintainable, production-ready code. His profile blends deep academic credentials with hands-on open-source impact in ML data and prompting infrastructure.
14 years of coding experience
Bachelor of Science (BS), Computer Science and Mathematics, Magna Cum Laude, Bachelor of Science (BS), Computer Science and Mathematics, Magna Cum Laude at Georgetown UniversityDoctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at University of Maryland
Github Skills (11)
orm10
sqlalchemy10
object-mapping10
jinjava10
python10
database-design10
jinja210
natural-language-processing9
ui-design9
machine-learning7
javascript5
Programming languages (7)
Github contributions (5)
bigscience-workshop/promptsource
May 2021 - Jul 2022
Toolkit for creating, sharing and using natural language prompts.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:179 reviews, 122 commits, 255 PRs in 1 year 1 month
Contributions summary:Stephen contributed to the development of a natural language prompting toolkit. Their work primarily involved enhancing the UI for template creation and dataset viewing. The commits demonstrate an understanding of web development technologies and data structures, by implementing new UI features for template editing, including adding metadata, and language tags.
nlppromptspythontransfer-learningsharing
snorkel-team/snorkel
Jul 2016 - Jul 2018
A system for quickly generating training data with weak supervision
Role in this project:Back-end Developer
Back-end DeveloperContributions:4 releases, 467 commits, 57 PRs in 1 year 11 months
Contributions summary:Stephen implemented a database backend for the parser classes, including an object-relational mapping (ORM) setup using SQLAlchemy. This involved defining database models for the core data structures like `Corpus`, `Document`, and `Sentence`, as well as the relationships between them. Additionally, the user modified existing files in the `examples` directory, showing a direct involvement with core data models for parsing and information extraction.
weak-supervisionpythondata-sciencemachine-learninglabeling
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