Peter Organisciak

Associate Professor, Research Methods and Information Science, University of Denver

I work on creativity and AI, as well as massive-scale text analysis. I'm on research sabbatical until September 2024, building new and interesting things.

See my CV, or find me and the Massive Texts Lab on Github.

Check out online tools: Open Creativity Scoring for scoring tests of creativity, SaDDL for digital library book relationships, and HT+Bookworm for exploring historic language trends.

Recent Research

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Scoring creativity with Large-Language models greatly improves on state of the art models.

Studying creativity is challenged by the difficulty of measuring and scoring tests of originality. We improved on automated scoring of one common test, the Alternate Uses Task, to a large degree. Read the paper

Artificial data can address class imbalance in digital library classifiers.

We seek to identify whole-part relationships between books, such as when one story is published in another anthology. This type of relationship is hard to infer from cataloguing metadata, but we find that constructing artificial books can teach a deep neural network classifier what the relationship looks like. Read the paper


See my papers on Google Scholar. I once wrote about crowds and text at Sense and Sentences.


Contact me to inquire about creativity and AI, data mining and machine learning assistance. I like playful projects and am based in the Denver area.