Dan Huff
Retired at Google Research
Blacksburg, Virginia, United States
Summary
🤩
Rockstar🎓
Top SchoolDan Huff is a retired engineer with 19 years of technical experience who transitioned from hands-on mechanical and electronics design to contributing to ML and NLP research codebases such as Google DeepMind and the high-profile google-research/language Canine project. His background spans motor and power-electronics product development, lab and facilities management at Virginia Tech, and manufacturing process engineering, giving him a rare mix of systems-level hardware expertise and software/ML engineering. On GitHub he has improved core ML model implementations and foundational toolkits (NLTK, DyNet), demonstrating attention to clean, modular code and low-level computational correctness. Based in Blacksburg, Virginia, he pairs practical prototyping and productionization experience with research-oriented contributions to natural language processing. A Vietnam-era Air Force veteran, he blends disciplined operational rigor with curiosity-driven problem solving across disciplines.
18 years of coding experience
16 years of employment as a software developer
Virginia TechAssociate of Arts and Sciences (AAS), Mechanical Technology, Associate of Arts and Sciences (AAS), Mechanical Technology at Mohawk Valley Community College
Stackoverflow
52,563reputation
3.0mreached
565answers
16questions
mutable
top-5%
dictionary
top-5%
grammar
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java
top-5%
text-mining
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jar
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Github Skills (54)
c-language10
python10
model-driven10
memory-management10
machine-learning10
pattern-matching10
scala10
model-building10
tensorflow10
parsing10
natural-language-processing10
neural-network10
nlp10
cprogramming-language10
model-driven-development10
Programming languages (7)
Github contributions (5)
nltk/nltk
Jun 2007 - Jun 2015
NLTK Source
Role in this project:
Back-end Developer
Back-end DeveloperContributions:361 commits, 1 PR, 3 pushes in 8 years
Contributions summary:Dan contributed to the glue semantics code, making updates to accommodate new feature structure changes. The code modifications involved changes in the contributions/gluesemantics directory, specifically to the glue.py, linearlogic.py files. The changes mainly involved adjustments for incorporating first-order predicate logic, code cleanup, and fixing issues related to grammar and parsing. These modifications directly impacted the implementation of the glue semantics and overall parsing functionality.
nlppythonmachine-learningnltknatural-language-processing
google-research/language
Apr 2021 - Apr 2022
Role in this project:ML Engineer
ML EngineerContributions:10 commits, 15 pushes, 4 issues in 1 year
Contributions summary:Dan primarily contributed to the `language/canine` project, focusing on the implementation and refinement of the Canine model. Their work involved modifying the model configuration, fixing bugs related to the `type_vocab_size`, deleting dead code, and simplifying the code by removing unused variables and methods. Furthermore, they extracted upsampling into its own method, demonstrating a focus on modularity and code clarity within the model's architecture.
nlplanguage-teamlanguage-aimachine-learningnatural-language-processing
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