I turn emerging AI capabilities into working, testable product experiences—bridging interaction design, system logic, and front-end prototyping.
Change the intent, choose a control model, and run the simulated workflow.
A selection of AI-native workflows and complex systems. Each project combines product framing, interaction architecture, and high-fidelity prototyping.
A connected AI system that turns inspiration into personalized, explainable actions across the creator journey.
A dual-role learning system that translates dense performance data into explainable priorities for students and actionable intervention paths for instructors.
A human-in-the-loop authoring system for reviewing, editing, and reversing AI changes without losing user intent.
Connect user intent, emerging capability, and the constraint that actually shapes the experience.
Define decisions, transitions, errors, reversibility, and the boundaries of automation.
Move into code when static frames can no longer answer the product question.
Use realistic content and edge cases to expose confusion before production hardens it.
Translate successful behavior into components, patterns, and technical documentation.
High-fidelity responsive experiences with deliberate motion, component logic, and accessible interaction states.
Agentic workflows, explainable recommendations, progressive control, memory models, and recovery from imperfect output.
Complex workflows translated into comprehensible states, reusable patterns, responsive components, and design-to-code contracts.
Working artifacts that align design, product, research, and engineering around what the experience should actually do.
I’m interested in teams working at the edge of product design, emerging technology, and human judgment.