Hamel Husain
Software Engineer at Parlance Labs
Portland, Oregon, United States
Summary
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RockstarHamel Husain is a Portland-based Software Engineer with 11 years of experience focused on machine learning systems and full‑stack development. He’s an active open-source contributor across the fastai ecosystem and projects like axolotl, fastpages, and nbdev, working on everything from prompt tokenization for Llama2/Mistral to distributed training support. Hamel bridges notebooks and production—refactoring nbdev/fastcore, building a FastAI-based semantic code search, and improving test coverage and documentation to make research reproducible. His ML work includes improving IoU evaluation and adding confusion-matrix diagnostics, plus integrating model configs with Weights & Biases for better experiment tracking. Comfortable across frontend, backend, and model-formatting edges, he excels at turning cutting‑edge ML research into reliable developer-facing tools.
12 years of coding experience
Stackoverflow
1,288reputation
115kreached
4answers
4questions
apache-spark
top-5%
Github Skills (56)
api-doc10
transformers10
markdown10
tokenize10
notebook10
pytorch10
documentations10
python10
code-search10
evaluation10
data-science10
testing10
semantic-segmentation10
machine-learning10
quarto10
Programming languages (20)
Github contributions (5)
fastai/fastpages
Feb 2020 - Nov 2022
An easy to use blogging platform, with enhanced support for Jupyter Notebooks.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:18 releases, 26 reviews, 1177 commits in 2 years 9 months
Contributions summary:Hamel appears to be a full-stack developer, heavily focused on the blog and documentation system. The commits suggest work on improving the system through code refactoring, and also implementing user-facing features, such as adding comment sections. The user's contributions span both frontend and backend aspects, with changes to both HTML and Python files, along with documentation updates.
literate-programmingvisualizationpythondata-sciencejupyter-notebook
hamelsmu/code_search
May 2018 - Jun 2018
Code For Medium Article: "How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning"
Role in this project:Data Scientist
Data ScientistContributions:91 commits, 18 PRs, 19 pushes in 19 days
Contributions summary:Hamel contributed to the development of a language model for code search, focusing on building and updating the language model using FastAI. The commits demonstrate the pre-processing of data, model training, and generation of embeddings. These efforts appear to be focused on constructing a semantic search engine for arbitrary objects using deep learning techniques.
pythonsemantic-search-enginetensorflownatural-language-processingdata-science
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