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Achievements

Achievement: Pull SharkAchievement: Arctic Code Vault Contributor

Achievements

Achievement: Pull SharkAchievement: Arctic Code Vault Contributor

Highlights

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Organizations

@DALME @Harvard-DSSG @harvard-digital-history

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gpizzorno/README.md

Gabe Pizzorno

I build systems that turn messy, unstructured source material into machine-actionable data—entity resolution, knowledge graphs, ontologies, information extraction, and retrieval architectures—and the production platforms that put that data to work.

🌐 Bio • ✉️ Email

Stack

Languages Python | JavaScript/TypeScript | SQL | C | Shell
ML/AI PyTorch | scikit-learn | HuggingFace | spaCy | Stanza | LlamaIndex | sentence-transformers
Data Pandas | NumPy | Elasticsearch/OpenSearch | PostgreSQL | Neo4j | RDF/OWL/SPARQL
Backend Django | DRF | Celery | Docker | Terraform | AWS (EC2, S3, Lambda, RDS, ECS, OpenSearch)
Other OpenCV | Tesseract | OR-Tools | NetworkX | D3 | GeoPandas | QGIS

Pinned Loading

  1. correction-as-annotation correction-as-annotation Public

    Human-in-the-loop bootstrapping of a dependency parser for a low-resource historical corpus: 0.48 → 0.92 LAS in 33 annotation hours, using 97% less training data than the best baseline. Includes th…

    Python 3 2

  2. DALME/DALME-Online-Database DALME/DALME-Online-Database Public

    A digital environment designed to facilitate the extraction, analysis, and publication of material culture information from textual primary sources.

    Python 11 3

  3. conllu-tools conllu-tools Public

    A Python toolkit for working with CoNLL-U files, Universal Dependencies treebanks, and annotated corpora.

    Python 6 3

  4. rules-based-entity-extraction rules-based-entity-extraction Public

    A pipeline for extracting unnamed entities from Medieval Latin texts by combining rule-based resources and a machine learning chunker trained on custom features. It supports evaluation, visualizati…

    Python 2 1

  5. course-scheduling-optimization course-scheduling-optimization Public

    This repository outlines a programmatic solution to the problem of course scheduling under institutional constraints. By modeling the problem as a MIP and carefully constructing the cost matrix, it…

    Python

  6. baudot-murray-CCIR476-demo baudot-murray-CCIR476-demo Public

    Demo workflow for digitally decoding Baudot-Murray/CCIR 476 encoded teleprinter tape from microfilm images using computer vision techniques.

    Jupyter Notebook