Evaluate conversational AI outputs for quality, safety, empathy, scope control, and user comprehension. Translate clinical communication patterns into human-in-the-loop feedback for safer AI system behavior.

Practice Intuition began as a training philosophy as a biomechanics specialist & movement coach. Augmented by design language and human behavior, it evolved into an integrative framework at the intersection of the product development lifecycle & the human systems feedback loop. With a coherent and unified language, we can implement critical and factored systems. Independently building a holistic coaching app - cognitive, biomechanical, behavioral systems integration. Through understanding the human experience as a set of control systems, I help individuals regulate, train, and express themselves. Learning is Unobstructed Self Expression. 2026 - Human-Systems Interaction Designer - Founder of Practice Intuition 2025 - Human Factors + Mixed Methods Researcher - Cognitive & Behavioral Analysis 2024 - Movement Coaching & Biomechanics Specialist - NASM Certified, GOATA, Biomechanics Specialist 2023 - CodingDojo & AWS Certifications - Software Developer + Cloud Infrastructure - DCF Paper Submission for Practice Intuition (training) 2022 - Emergency Medical Technician & Patent Inventor - Clinical Background & Biomedical Engineer

Designed and executed think-aloud usability studies evaluating mobile product discovery, conversion friction, purchase drivers, and retention levers. Identified 5 high-severity usability issues impacting 80% of test participants and developed psychographic segmentation model connecting cognitive traits, identity-based values, behavioral variables, and personas.

Clinical Roleplay Simulator & Communication Solution -- 0-to-1 Product Development: owned full product lifecycle from concept validation to shipped prototype: identified gap in market, designed lo-fi prototype (GPT-4o), conducted feasibility testing, delivered MVP -- Architected modular, extensible conversational UI system integrating LLM-based agents, retrieval-augmented generation, & multimodal interaction capabilities (ASR, STT/TTS, video avatars) -- Developed prompt engineering framework generating contextually grounded clinical scenarios and dynamic patient personas: fine-tuned the adaptive dialogue changes, turn-taking logic, context window -- Designed psychologically grounded patient-personas encoding behavioral traits, cognitive patterns, and decision-making frameworks that AI agents replicate through state-based architecture -- Constructed evaluative feedback for medical professionals and clinicians to develop patient-centered communication skills, including motivational interviewing protocols while maintaining clinical competency