Peter Harrington
Co-Founder And CTO at LexText.AI
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolPeter Harrington is Co-Founder and CTO in the San Francisco Bay Area with 14 years of experience building high-performance predictive analytics, NLP, and information-retrieval systems. He combines hands-on ML and data engineering leadership—designing forecasting engines, information-extraction pipelines, and entity-linking tools—with product and infrastructure ownership across startups and enterprises such as LexText.AI, HG Data, and Micron. Unusually, his background spans both semiconductor design and early post‑silicon testing as well as large-scale ML product delivery, enabling him to bridge hardware-aware testing and software-driven analytics. He co-founded Zillabyte and led end-to-end data product development, and has contributed practical implementations to the well-known "Machine Learning in Action" codebase (kNN, SVMs, AdaBoost and more). With graduate study in software and electrical engineering, he excels at turning research-grade algorithms into cost-effective, production-ready systems.
14 years of coding experience
17 years of employment as a software developer
MSEE, Electrical Engineering, MSEE, Electrical Engineering at Arizona State UniversityMasters of Liberal Arts Extension Studies, Software Engineering, Computer Software Engineering, Graduate, Masters of Liberal Arts Extension Studies, Software Engineering, Computer Software Engineering, Graduate at Harvard UniversityBSEE, Electrical and Computer Engineering, BSEE, Electrical and Computer Engineering at Purdue University
Chinese
Github Skills (7)
decision-tree10
logistic-regression10
machine-learning10
adaboost10
naive-bayes10
python10
numpy9
Programming languages (4)
Github contributions (5)
pbharrin/machinelearninginaction
May 2011 - Feb 2016
Source Code for the book: Machine Learning in Action published by Manning
Role in this project:
ML Engineer
ML EngineerContributions:58 commits, 2 PRs, 1 push in 4 years 10 months
Contributions summary:Peter primarily contributed to the codebase by adding, modifying, and refining code for machine learning algorithms. Specifically, the user added and updated multiple Python files implementing k-Nearest Neighbors (kNN), decision trees, Naive Bayes, Logistic Regression, Support Vector Machines (SVMs), and AdaBoost. These contributions included implementing core functions, adding new datasets, and improving the overall functionality of various machine learning models, providing a comprehensive suite of ML techniques.
pythonmanningdata-sciencemachine-learning-bookmachine-learning
pbharrin/pyconometrics
May 2012 - Oct 2012
Contributions:9 commits in 5 months
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