Senior Applied & Data Scientist @ NetApp · AI Governance & Safety Educator · Researcher
I build production AI systems — and teach the people who govern them.
RAG, agentic frameworks, and responsible-AI evaluation.
I'm an applied AI scientist working at the intersection of production machine learning and AI safety, governance, and ethics.
At NetApp, I built the company's first customer-facing Retrieval-Augmented Generation (RAG) system and lead internal AI initiatives in security and governance for enterprise storage products. Alongside that, I teach graduate courses on responsible AI, agentic frameworks, and the ethics of data science at NC State and UNC–Chapel Hill, and I'm writing a book on AI and culture for Bloomsbury Academic.
My background is deliberately interdisciplinary: a Ph.D. in the humanities alongside hands-on ML engineering. I ship models and reason rigorously about their failure modes, fairness, and societal impact.
- Applied AI / LLMs — RAG pipelines, agentic systems, prompt engineering, production deployment
- AI safety & governance — responsible-AI frameworks (NIST AI RMF, EU AI Act), bias & fairness audits, model cards, human oversight
- LLM evaluation — measuring quality, safety, and hallucination in model outputs
- ML for research — NLP, computer vision, and network analysis over large cultural datasets
| Project | What it is |
|---|---|
| data-advanced-ai | A full graduate course (MBA 590) on advanced AI strategy — prompting, RAG, agentic & multi-agent systems, LLM evaluation, and AI governance, with runnable notebooks. |
| An-Adaptive-Methodology | A machine-learning method for detecting literary adaptation at scale, from my dissertation (presented at Digital Humanities 2022, Tokyo). |
| gitarchaeology | Research on survivorship bias in open datasets and how to build reproducible, historically faithful research corpora. |
| intro_to_ml | "Machine Learning for Humanists" — a hands-on introduction to ML I developed for the TAP Institute. |
| social-media-workshop | Methods and materials for computational social-media research in Python (DHSI workshop). |
Languages: Python · SQL · Bash AI / ML: LLMs · RAG · agentic frameworks · NLP · prompt engineering · scikit-learn · GANs · model evaluation Platforms: Azure AI · AWS · Docker · Jupyter · Git Governance: NIST AI RMF · EU AI Act · bias & fairness audits · model cards
Microsoft Certified: Azure AI Engineer Associate
- 📖 Literary Culture in the Age of AI: Agents of the Algorithm — Bloomsbury Academic (under contract)
- 📄 "Visions in the Machine: Automated Tagging of the William Blake Archive" — Digital Humanities Quarterly (2026)
- 📑 "Enhancing RAG Systems: Lessons from Doc Development at NetApp" — NetApp white paper (2024)
- 🔗 On the Books: Jim Crow and Algorithms of Resistance — machine learning applied to historical legal text
glassgrant.com · ORCID · [email protected]
