Before design, I studied molecular biology with a focus on research. While my career took a different path, the way I learned to approach problems never really left.

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I still start with questions, look for patterns, and break complex problems into smaller pieces. It’s a mindset that naturally found its way into my design practice, especially when I’m working through ambiguity, complex workflows, or systems-level problems.

My process changes with the problem, but my approach stays consistent. I start by understanding how the pieces connect, where complexity comes from, and what actually needs to be solved.

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From there, I move between research, design, prototyping, and testing as the work requires, using AI throughout the process to explore faster, challenge assumptions, and spend more time on the decisions that need human judgment.

I map the people, workflows, patterns, constraints, and dependencies around a problem before narrowing in on a solution. Understanding how the pieces connect helps me solve the root problem instead of designing around its symptoms.

I look for opportunities to turn individual solutions into reusable patterns and foundations. That might mean simplifying a workflow, establishing a component pattern, or creating a system that gives teams a clearer way to build and scale.

I use AI throughout my workflow to synthesize information, explore directions, prototype ideas, and automate repetitive work. It helps me move from question to experiment faster, while research, context, critique, and human judgment guide what moves forward.