I am an AI Researcher and Applied Engineer bridging the gap between theoretical alignment and production reality. Currently the Director of AI at US AI, I build "telescopes" for observing model behavior—from national-scale observational pipelines to psychometric evaluation hubs.

• Mission: To treat AI safety as an empirical science, using rigorous measurement to understand how models impact human wellbeing.
• Philosophy: "Understanding the 'latent' traits of ourselves and others is the key to breaking down barriers and fostering empathy."

My research at the University of Tennessee focuses on Personality-Aware AI and the PRISM Protocol. I treat agents as latent-state dynamical systems, evaluating how trait-based conditioning impacts behavior, alignment, and coordination.

An experimentation hub for evaluating agent behavior across five rigorous research pillars. It repurposes narrative grounding (BookNLP) into actionable personality evidence.

• PRISM Protocol: A trait-state protocol for agents involving State Vectors ($P_S$), Trajectories ($\Delta P_S$), and Valence ($V$).
• Experimental Pillars:
  1. Trait Realization: Validating grounded character trait inference.
  2. Perturbation Stability: Testing resilience under scripted contradictions.
  3. Matched Performance: Aligning personality with task success (AgentBench).
  4. Multi-Agent Coordination: Team dynamics and communication efficiency.
  5. Narrative Dynamics: Arc stability and social emergence.
• Key Metrics: Psychometric Agreement (PA), Trait Discriminability (TD), Drift Magnitude (DM), and Collapse Time (CT).

A machine-readable taxonomy of 44 psychometric models (Submitted to ACM TIST, 2025).

• Standardization: Mapping models into a 5-part lexical schema (Factor, Adjective, Synonym, Verb, Noun).
• Scope: Covers Trait-Based (OCEAN/HEXACO), Narcissism, Clinical/Health, and Interpersonal models.
• Artifact: Personality-Trait-Models — Foundational library for optimizing recommender systems and latent trait modeling.

A complete graduate-level curriculum (COSC 650, UTK) covering the practical applications of Generative AI.

• Modules: RAG Foundations, Advanced Prompt Engineering, Agentic Workflows (Plan-and-Execute), and Constitutional AI.
• Resources: 400+ curated research papers, 50+ notebooks, and custom "Markdown Cards" for LMS integration.

A 10-module curriculum designed for students bridging the gap from basic statistics to production machine learning.

• Highlights: Statistical programming in R, Big Data (DS107), and SQL/NoSQL Databases (DS108).

• Role: Lead Architect & Implementer.
• Impact: Deployed the VA’s first GenAI pipeline handling 1.5M+ daily clinical notes.
• Safety: Detects Social Determinants of Mental Health (SDoH) to improve veteran outcomes through closed-loop AI observation.

• $1.34M AVIN Innovation Grant: Integrating personality models into autonomous systems.
• $1M ENCQOR 5G Grant: AI/ML behavioral integration in connected corridors.
• VA Innovation Award: For the CLEVER Pipeline & AI-Assistant.
• Scientific Achievement Award: Critical mission research.

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