Product and design leadership for complex systems.

I help organizations turn complexity — technical, organizational, and human — into decisions organizations can commit to with confidence. My work spans product strategy, design leadership, human-AI workflow design, and cross-functional alignment across enterprise software, scientific platforms, and AI tools.

This work has typically operated at the level of enterprise modernization — aligning product, engineering, and business leadership around a single strategic direction before implementation begins.

My Practice

I think of design as a translation discipline.

Design translates current customer experiences into product improvement opportunities.

  • Customer behavior → Improvement opportunities
  • How experts work → User-centered products
  • User workflows → Information architecture
  • Workarounds → Unmet needs

What experience has taught me

01

Complexity is structural before it's visual.

Most confusing products are organized around systems instead of the work people are trying to do.

Software often takes the shape of the system that built it, not the work it's meant to support — organized around instruments or departments instead of the task at hand. That confusion isn't cosmetic, so it resists cosmetic fixes. In an eighteen-year-old research platform and a decades-old safety-control system, the real improvement came from reorganizing the structure around how people actually work — not a better interface on top of the same one.

02

Confidence should come after evidence, not before it.

Most roadmaps turn assumptions into commitments before anyone tests them.

Certainty is easy to manufacture and hard to justify. A direction repeated in enough meetings starts to sound like fact, even when nothing has actually been tested. The costliest mistakes I've seen weren't failures of execution — they were assumptions nobody questioned before they hardened into commitments. Holding a decision open long enough to test it, even under pressure to move faster, is where the real difference lies.

Selected Work

Three projects where the hardest problem wasn't the interface — it was getting everyone to agree on what problem they were actually solving.

Transforming a Mission-Critical Scientific Platform

Defined the modernization strategy and workflow-centered architecture for an 18-year legacy platform used by materials scientists—reframing the product beyond instrument-centric design and aligning product, engineering, and business stakeholders around a shared direction.

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TA Instruments information architecture

My role: Defined the workflow architecture and modernization strategy for reframing the platform around scientists' research processes, and built the cross-functional alignment behind it.

AI-Assisted Decision Support for Materials Research

Defined product strategy and design for an AI-assisted informatics platform that helped scientists navigate complex material composition spaces and accelerate discovery. Bridged advanced ML capability and research workflow to create a tool scientists could trust and act on.

10–25% faster scientific discovery

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Corning Materials Informatics data visualization

Helping materials scientists navigate complex composition spaces through interactive visualization.

Designing the Human Layer in an AI Workflow

Led product vision and design strategy for a professional curation platform that orchestrated 30+ ML algorithms into a calibration-driven workflow. Reduced review time from approximately 18.5 hours to about 1 hour — not by automating photographer judgment, but by making AI recommendations legible, adjustable, and trustworthy.

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Curator AI photo curation workflow

From 30+ Algorithms to One Curator Experience
Designed the orchestration layer that transformed machine learning outputs into a curator-controlled workflow, reducing review time from approximately 18.5 hours to about 1 hour.

Perspectives

These articles diagnose the organizational patterns that most reliably limit product and design impact — and describe how to address them. They reflect the same approach I bring to practice: understanding why a system produces its current results before trying to change it.