AI product leadership · trustworthy AI
I help organizations transform uncertainty into AI products people trust.
Hands-on AI product builder and trustworthy AI researcher.
Open to product roles in startups and mission-driven teams where I can build, learn, and create meaningful change
01 · How I think
My operating system is a practical loop for leading through ambiguity—shaped by startups, healthcare, research, manufacturing, AI operations, and civic response.
When evidence is incomplete, design the next decision that creates evidence.
Stage 01
Learn the people, incentives, users, workflows, and constraints. Look for what behavior reveals and what people are not yet saying.
02 · Selected transformations
I am drawn to environments where the problem is important, the path is unclear, and the system—not only the feature—needs to change.
Verified professional record
Hardware execution had stalled. The deeper constraint was not engineering capability; it was the system around it.
Applied AI project
The harder question was not whether a model could classify sound, but how to make that information useful without adding distraction.
Working research
A deterministic evaluation instrument for multi-turn sycophancy—without relying on another model as judge.
Evidence labels distinguish documented results, applied project benchmarks, and working research. Confidential or unverified details are not published.
03 · Trustworthy AI
I study how AI systems behave when truth, agreement, and human expectations come into tension—and translate that behavior into product decisions.
Does it improve a meaningful human outcome?
Does it perform consistently where it will be used?
Does it resist agreeing when evidence is weak?
Can people review, override, correct, and escalate?
A deterministic instrument for measuring when language models shift toward user agreement and away from evidence.
04 · Original frameworks
Tools for moving from ambiguity to evidence, alignment, and durable execution. Each is anchored in practice and includes its limits.
Symptoms are mistaken for root causes.
Decisions rely on confidence instead of learning quality.
Stakeholders use different definitions, goals, or decision rights.
AI teams treat trust as an abstract value.
05 · Writing & research
Speaking
I have spoken through TEDx, UNESCO, Microsoft, SAP Women in Tech, panels, and podcasts about AI, product transformation, and technology for human capability.
About me
My mother asked me to water the plants every day. I built a system to do it automatically.
That small project revealed a pattern that has followed me through AI, healthcare, manufacturing, agriculture, cybersecurity, and research: when a repeated problem depends on memory, heroics, or disconnected effort, I look for the system underneath it.
Start a conversation
I’m looking for hands-on product opportunities in startups and teams where I can work close to the problem, help shape the product, and make visible change. I’m also open to research collaboration, speaking, and thoughtful advisory conversations.