John Bauer
Research Programmer at Stanford University
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJohn Bauer is a Research Programmer with 13 years of experience bridging academic NLP research and production-grade engineering, currently based in Palo Alto and working at Stanford University. He has deep expertise in core NLP tooling—contributing to well-known projects like Stanford CoreNLP and the GloVe word-embedding implementation—where he improved tokenization, multilingual parsing, Unicode handling, performance, and test automation. His background spans research roles at Stanford and industry positions at Vicarious and MetaMind, combining rigorous MS-level CS training with practical system optimizations in C and Java. Known for quietly strengthening robustness and memory efficiency behind the scenes, he brings a pragmatic focus on tooling, datasets, and reproducible evaluation to make research code reliable in real-world use.
13 years of coding experience
6 years of employment as a software developer
MS Computer Science, MS Computer Science at Stanford University
Github Skills (32)
unit-testing10
named-entity-recognition10
testing10
machine-learning10
c1110
java10
datasets10
c1710
javas10
dependency-parsing10
lexical-analysis10
natural-language-processing10
text-processing10
nlp10
algorithm9
Programming languages (17)
Github contributions (5)
stanfordnlp/CoreNLP
Jul 2013 - Jan 2023
CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:12 releases, 13 reviews, 4179 commits in 9 years 8 months
Contributions summary:John's contributions focus on improving and refining the core functionality of the CoreNLP project, specifically related to the Chinese and other languages' sentence parsing and tokenization, as demonstrated by code modifications within the tokenizer and sentence splitting modules. The user addressed issues and integrated features for Unicode text handling, and implemented a number of tests using tools like JUnit, while refactoring the core code to improve efficiency and memory usage. They addressed some parser errors, and implemented tools to help score and refine the performance of models.
word-embeddingssentiment-analysisnatural-language-processingstanford-nlpsuite
stanfordnlp/GloVe
Nov 2019 - Nov 2022
Software in C and data files for the popular GloVe model for distributed word representations, a.k.a. word vectors or embeddings
Role in this project:Back-end Developer
Back-end DeveloperContributions:1 review, 34 commits, 29 PRs in 3 years 1 month
Contributions summary:John primarily contributed to the core functionality and stability of the GloVe model implementation in C. They fixed compilation and build issues, addressed file loading errors, and implemented error handling. Further contributions include code improvements such as optimizations related to memory allocation, and fixing issues related to Python 3 compatibility, indicating their focus on the project's robustness and functionality.
nlprepresentationk-meansword-embeddingssentence-similarity
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