Senior Software Engineer & DataOps Lead | OMSCS (AI Track) | CISSP
Applied ML • OSINT/Security Data • Python Automation • Reproducible Systems

Engineer with ~a decade of experience building AI/ML enrichment pipelines and automation for federal security programs. I design reproducible, operationally grounded systems that turn messy real-world data into something models can use — and I publish peer-reviewed research on trustworthy AI and security.

My work sits at the intersection of:

• ML/LLM data enrichment pipelines
• Security & OSINT data analysis
• Python-based automation at scale
• Reproducible, trustworthy system design

• Trustworthy LLM-agent workflows / source attestation — in-progress IEEE work with Prof. Vijay K. Madisetti (Fellow, IEEE)
• CPU side-channel analysis — ICSC 2024
• Insider-threat detection — Springer, 2021

• Provenance & attestation for trustworthy AI-agent workflows
• Reproducible ML/LLM evaluation pipelines
• Applying ML to security and operational datasets
• Digital accessibility in computing (see below)

• harrystaley.github.io — static GitHub Pages portfolio and project index.
• staleyh.us — custom-domain site for long-form posts and publishing workflows.
• github-profile-readme-generator — tooling for structured, maintainable profile READMEs.
• shell_setup — reproducible shell/bootstrap environment setup.
• open-source-cs-python — curated, open-source CS learning path in Python.

• Vice President, National Federation of the Blind — Computer Science Division
• Vice President, National Federation of the Blind — Science & Engineering Division
• Proposing a graduate Digital Accessibility course (OMSCS)

Languages: Python, Bash, SQL ML / Data: Pandas, spaCy, scikit-learn, FastAPI, OpenSearch, Airflow; data enrichment & evaluation pipelines Security: CISSP; OSINT tooling, network labs, defensive infrastructure Tools: Linux • Git • Docker • Cloud