Product Design and Human Factors Leader
I find the hard problem in regulated medical devices, enterprise AI, and spatial research, then design for the case where a mistake is a consequential event, not a missed click
Previously:
The problem first. Full case studies on request.
Most enterprise AI stalls between a promising demo and real production, blocked by unclear task boundaries and weak review before an agent acts. I led UX on Forge, an E2E agent platform built against those exact adoption blockers, grounded in generative research with enterprise process owners, designing the surfaces where a person stays able to see, approve, and override what the system does.
AppSheet was a mature no-code platform that couldn’t absorb AI by bolting it on or rebuilding from scratch. I led UX Design across seven product surfaces, from AI automation to governed MCP agent access, validated through a mixed-methods, ANOVA-backed study, modernizing how people work in it while protecting the depth enterprise customers depend on.
Arthrex’s surgical products had grown over decades, each device designed independently, which raised cognitive load in the OR. I led human-factors research and product design across a seven-product family of handheld devices, consoles, and OR integration software, anchoring control logic and feedback to anthropometric, usability, and use-error evidence under IEC 62366 and FDA design controls.
Across radiology reading, consumer evaluation, and OR training, the same failure recurred: when information moves away from the task, people have to reconstruct context themselves. I ran eight years of peer-reviewed research and design, including eye-tracking and controlled studies, on how to keep it in place, leading the spatial design.
I look for the problem first. The place where getting it wrong has a real cost, and I design for that case, not the happy path.
I started in industrial product design, where a bad decision ships in metal and plastic and can’t be patched later. That’s still how I think about software, including the years I spent designing for Google Cloud AI.
Before Google, I spent four years as a tenure-track Assistant Professor of Product Design at the University of Minnesota. The research habits stuck: I hold a Ph.D. from the University of Missouri-Columbia, my work on perception and presence is peer-reviewed, and I’m comfortable inside the rigor that regulated medical-device design demands.
Before the regulated systems and enterprise platforms, there was the bench: several years of industrial design, prototyping, and research craft. It’s the foundation the rest is built on.
If the cost of getting it wrong is real on your side, that’s the conversation I want.
Where Getting It Wrong Has a Cost
Where use-error is a clinical event, not a support ticket.
On how people actually interpret what they see.
