Connecting the people who build the future
The work runs across silos that rarely talk to each other: frontier AI labs, industry analysts and investors, government standards bodies, multilateral institutions, and nonprofits. Building those channels is part of the practice.
OpenAI higher-education forums; the Meta Open Innovation AI Research Community (since 2023); the Notre Dame–IBM Technology Ethics Lab.
Multi-year participation (since 2023) in live BWG forums spanning the full AI-market agenda: public LLMs, OpenAI, DeepSeek, Anthropic, Gemini, agentic adoption, benchmarking, enterprise deployment, and regulation.
Co-founder and technical lead of the team representing the Modern Language Association at the NIST Center for AI Standards and Innovation, on LLM evaluation and red-teaming.
Engagement with UNESCO and United Nations conversations on global AI governance.
Interdisciplinary roundtables at the Helix Center; co-founder of the nonprofit Human-Centered AI Lab.
Co-PI on Schmidt Sciences HAVI (1 of 23 teams from 600+ applications) and the Notre Dame–IBM Technology Ethics Lab grant (1 of 11 internationally, $60,000, with Notre Dame's Yong Suk Lee); joint papers with collaborators at Oxford. See recognition →
How ideas move
The point of holding seats in academia, industry, and government at once is that ideas can move between them. A question raised in an undergraduate seminar becomes a peer-reviewed method; the method becomes an ethics-based audit a federal consortium can use; the audit informs how enterprises deploy the same models; and what is learned in the market comes back into the next year's curriculum.
- Classroom → research. Student projects on narrative and LLMs grow into ICML, CoNLL, and AAAI papers.
- Research → standards. Ethics-based auditing and comparative regulation feed the MLA team's work at NIST CAISI.
- Standards → industry. The same evaluation lens travels into enterprise AI through BWG forums and a deployed multi-agent product.
- Industry → curriculum. Live market questions rewrite more than half of the agentic-AI course each year.
Computational narrative: a joint research program
Since 2019, Jon Chun and Katherine Elkins have built a shared research program on computational narrative — what stays the same and what changes when stories move across languages, translations, and cultures, and how machines can read narrative at multiple analytical distances.
The instruments are Chun’s: SentimentArcs (2021, arXiv 2110.09454), the ensemble pipeline for diachronic sentiment analysis with dynamic-time-warping comparison of unequal-length narrative arcs, and MultiSentimentArcs, its cross-modal extension. The interpretive and cross-cultural program is Elkins’s: The Shapes of Stories (Cambridge UP, 2022), “In Search of a Translator” (2024), “The Shapes of Cinderella” (2025). The joint work spans both: “Can Sentiment Analysis Reveal Structure in a Plotless Novel?” (2019), “Can GPT-3 Pass a Writer’s Turing Test?” (2020), and “eXplainable AI with GPT-4 for Story Analysis and Generation” (2023).
The full dated chronology: katherineelkins.com/narrative-translation-transmission →
BWG Global
BWG runs primary-research forums that bring practitioners, analysts, integrators, and investors into candid, real-time discussion of enterprise AI. Multi-year participation there is direct evidence that the work crosses from the classroom into live market decision-making.
"Participating in BWG forums since 2023 has informed my interdisciplinary work by providing candid, peer-level conversations with active decision-makers about real enterprise AI adoption."
Jon A. Chun, on bwgglobal.comArchival Intelligence
Co-PI on Archival Intelligence, building free, open AI tools that turn smartphone photography into a rescue method for New Orleans' endangered Creole and Cajun multilingual newspapers and early jazz artifacts, and that confront “cultural flattening” in AI models.
1 of 23 teams selected worldwide from 600+ applications for the inaugural Humanities and AI Virtual Institute ($11M program; award up to $330K), a highly selective global competition, among the top few percent of applicants. The cohort's peers include Harvard, Stanford, Princeton, Oxford, Cambridge, and the Sorbonne. Kenyon's AI CoLab is one of very few teams led from a small liberal-arts college, in a cohort otherwise dominated by major research universities.