Gordon Mohr
Shopkeep, 24-hour Comprehension Store at Thunkpedia et al
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolGordon Mohr is a seasoned software engineer and founder with 14+ years crafting distributed systems, web archiving tools, and NLP/ML infrastructure from San Francisco. He built and led open-source web-archiving efforts at the Internet Archive, advised on large-scale crawls, and later contributed core functionality and inference features to Gensim—improving Word2Vec/Doc2Vec performance and similarity models. As founder of Bitzi and operator of Thunkpedia’s “24-hour Comprehension Store,” he blends product-minded entrepreneurship with deep technical design in content-addressing, messaging, and P2P systems. His background spans hands-on backend engineering, data science, and tooling for extraction of massive corpora (notably Wikipedia extraction), reflecting a long-term focus on preserving and making sense of information at scale. He holds a BA in Economics and Computer Science from UC Berkeley and brings a pragmatic, research-informed approach to applied machine learning and decentralized technologies.
14 years of coding experience
17 years of employment as a software developer
BA Economics and Computer Science, BA Economics and Computer Science at University of California, Berkeley
Stackoverflow
53,191reputation
5.9mreached
1,556answers
34questions
html
top-1%
nltk
top-5%
nlp
top-1%
python
top-1%
machine-learning
top-1%
decimal
top-5%
Github Skills (37)
word2vec10
python10
css10
machine-learning10
word-embeddings10
natural-language-processing10
file-handling10
neural-network10
html10
topic-modeling10
nlp10
data-science9
algorithm9
python-multiprocessing9
algorithms9
Programming languages (16)
Github contributions (5)
piskvorky/gensim
Jun 2014 - Dec 2022
Topic Modelling for Humans
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:94 reviews, 181 commits, 74 PRs in 8 years 7 months
Contributions summary:Gordon implemented a new 'sample' parameter in the `Word2Vec` model to downsample frequent words, improving performance. They also added support for the multiplicative objective (3CosMul) of Levy & Goldberg, improving the similarity calculation. Furthermore, the user introduced initial inference support for the `Doc2Vec` model and developed support for the `dm_concat` (concatenative PV-DM) model by adding pure-python code, including training and inference functionality.
pythonword-similarityword-embeddingsdata-miningfor-humans
attardi/wikiextractor
Jun 2015 - Jun 2015
A tool for extracting plain text from Wikipedia dumps
Role in this project:Back-end Developer
Back-end DeveloperContributions:6 commits, 1 PR, 2 comments in 2 days
Contributions summary:Gordon's contributions primarily revolve around modifying and improving the `WikiExtractor.py` script, which suggests a focus on the core functionality of the project. Their commits introduce changes to the processing pipeline, file handling, and template processing mechanisms. The user also made updates to multiprocessing and output formatting.
pythonwikipediaplainplain-textdumps
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