Behavioral health operations · clinical quality · policy implementation · applied AI safety
I am a licensed clinician and operations leader with 15+ years in behavioral health. My work spans clinical risk, quality improvement, mental health parity, training, and leadership of a team of 17 licensed clinicians. I am interested in roles where careful judgment, clear documentation, and dependable operations matter.
Portfolio · Resume · LinkedIn · Email
| Work sample | What it demonstrates | Explore |
|---|---|---|
| Case review quality lab | Evidence gaps, reviewer disagreement, urgent handoffs, and documented ownership across 16 fictional cases | Interactive queue · Code, tests, and decision memo |
| Safety evaluation operations | Paired evaluation analysis, critical regression review, and a launch gate that holds a candidate despite improved aggregate results | Dashboard · Reproducible Python and SQL |
| Policy and clinical quality examples | Review checklists, documentation maps, training briefs, and technical assistance | Work samples |
The technical projects use synthetic data and are portfolio demonstrations. They are separate from my professional experience and do not claim production trust-and-safety investigations, platform reporting, or independent engineering experience.
- Clinical quality and operations: risk review, documentation quality, clinician training, and cross-functional process improvement.
- Policy implementation: translating behavioral health requirements into evidence, review criteria, and practical workflows.
- Child protection foundation: a yearlong elementary-school counseling internship with mandated reporting, graduate work supporting practitioner training in sex-offender treatment, and forensic psychiatric volunteering.
- Education: M.Ed. from Ohio University; undergraduate double major in psychology and sociology/criminology.
These experiences inform my interest in child safety and responsible AI. They also support broader clinical operations, quality, compliance, and program roles.
Both projects run locally with Python's standard library. Each includes its own instructions, limitations, and tests. No API keys or confidential data are required.
The featured case study is human-led and AI-assisted. The professional context, constraints, risk framing, requirement for reproducibility, and final accountability belong to me. AI tools accelerated drafting, coding, and consistency checks. The project is explicit about synthetic data, limitations, and the distinction between a portfolio simulation and production trust-and-safety work.
I do not use confidential employer information, real user data, proprietary model results, or executable harmful prompts in this public repository.