Aaron Nitzkin - Computational Linguist

Aaron Nitzkin

Computational Linguist

Asheville, North Carolina, United States

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Summary

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Senior

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Top School

Aaron Nitzkin is a computational linguist with over a decade of interdisciplinary experience bridging linguistics, cognitive science, and software development. He has taught at the university level, contributed linguistics expertise to AGI work with the OpenCog Foundation, and applied his semantic and syntactic knowledge to debugging rule-based NLP in the open-source opencog/opencog project. As a freelance researcher, writer, and teacher since 2018, he produces academic and creative work while also coaching mind–body arts and composing music, reflecting a rare blend of analytical rigor and artistic practice. His PhD in interdisciplinary linguistics and language-teaching experience in China inform a comparative, cross-cultural approach to language problems. Colleagues value his ability to translate theoretical semantics into practical parsing fixes and research-ready code that improves handling of complex question types.

11 years of coding experience

11 years of employment as a software developer

BA, Russian Language and Literature, BA, Russian Language and Literature at University of Michigan

Ph.D., Interdisciplinary Linguistics, Ph.D., Interdisciplinary Linguistics at Tulane University

MA, English Language and Literature, General, MA, English Language and Literature, General at University of new Orleans

french, russian, mandarin, swahili, swedish, old english, yucatec, kaqchikel

Github Skills (5)

scheme10

natural-language-understanding10

agi8

algorithm7

algorithms7

Programming languages (3)

JavaC++Scheme

Github contributions (5)

opencog/opencog

Sep 2014 - Dec 2014

A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)

Role in this project:

Back-end Developer

Contributions:23 commits, 9 PRs, 100 comments in 3 months

Contributions summary:Aaron primarily contributed to the `opencog/opencog` repository by modifying Scheme code related to relex2logic rule-helpers. They debugged and tested example sentences, adding comments to document relex errors. The changes focused on improving the handling of yes/no questions and other question types within the natural language processing component. These contributions demonstrate the user's expertise in rule-based systems and their ability to identify and address semantic parsing issues.

unsupervised-learning-algorithmsroboticsnatural-language-understandingartificial-general-intelligenceintegrated

anitzkin/opencog

Feb 2015 - Jun 2015

Contributions:13 pushes, 5 branches in 4 months

artificial-general-intelligenceintegratedagiartificialmachine-learning

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