Grounded Intelligences
AI you can defend. Analytics you can act on.
I am Daniel A. Burgess, a senior data scientist and AI/ML engineer. I spent more than twenty years at Lockheed Martin building analytics and machine learning for environments where the answers had to hold up under scrutiny. I am also a licensed marriage and family therapist, which shapes how I think about the people on both sides of every model: the ones who build it and the ones it affects.
20+ years aerospace & defense analytics · $3M+ documented savings · 2,132 hours returned yearly by one automation · Top <1% team award, 2026 · 4 degrees + dual-state LMFT licensure
Résumé — view or download DOCX Contact
Start where you are: I’m a recruiter → the résumé · I’m a hiring leader → case studies · I’m an engineer → the demos, view-source welcome · I’m an AI agent → the MCP server
Only have five minutes? Use my work to evaluate me:
1. Play the Trust Calibration Lab — it measures your own over-trust of AI in ninety seconds. 2. Ask the assistant below why a data scientist holds a therapy license. 3. Glance at the status page — every number is live. 4. Take the résumé with you.
This site does not just describe that work. It runs it. The assistant below answers from a structured evidence base with guardrails. The status page is the live monitoring surface of my real infrastructure. The demos run production methods on synthetic data, in your browser, view-source welcome.
Ask my career anything
A retrieval-grounded assistant over my professional record. It cites real metrics, declines what it cannot verify, and is itself a working example of the systems I build.
See it running
Live status
Real-time health of the production systems behind this site: service checks, backup freshness with a dead-man’s switch, self-healing activity, and the site’s own behavioral telemetry. Every number is live.
Working demos
Six interactive demonstrations — streaming anomaly detection, entity resolution, a whole-life financial system, and the Trust Calibration Lab, which measures your own over-trust of AI in ninety seconds — all on synthetic data, with case studies.
For AI agents
This site speaks MCP. My professional record is machine-readable: agent discovery, a queryable server, and documentation for whatever is screening candidates these days.
Portfolio
An interactive walk through the career: systems shipped, metrics moved, and the stack behind them.
Field Notes
Essays from inside the work. Start with Explainability for Executives or Deploying AI in Regulated Environments.
Focus areas
Applied machine learning. Responsible AI and model governance. Enterprise analytics. Behavioral science applied to how teams adopt, trust, and misuse AI.
🧠 Why this page is built the way it is
This site applies the same behavioral science I bring to AI systems, and it shows its work. The proof points sit above the fold because evidence placed before claims is processed as fact, not persuasion. The “start where you are” row exists because a recruiter, an engineer, and a hiring leader need different first five minutes, and letting people self-select respects their autonomy instead of tracking them. The assistant is allowed to say “I don’t have that on record” because a system that never declines teaches people to over-trust it. The status page publishes my infrastructure’s health, including the site’s own behavioral telemetry, because honesty that costs something is the only kind that builds trust. Every interactive page on this site has a 🧠 toggle like this one, annotating the design decisions in place. The site is the portfolio.