Physician focused on measuring and validating health signals for technology products — from FDA-cleared neuroimaging to voice-based cognitive screening to wearable sensors. My background in medicine, biomimicry, and machine learning shapes how I think about ensuring these tools are both safe and effective.
Current Work
Currently focused on clinical validation frameworks for health AI — from voice-based cognitive screening to wearable sensors to ambient clinical intelligence. I advise companies and academic groups on healthcare AI, such as EpiFocus (seizure prediction via connectivity and chaos theory) and the Pitt HexAI Lab at the University of Pittsburgh.
Previously, as Chief Medical AI Officer at Scienza Health, I oversaw clinical validation and safety for conversational AI systems in healthcare. Before that, at Cortechs.ai, I worked on FDA-cleared neuroimaging software for Alzheimer's, multiple sclerosis, and brain tumor assessment — translating quantitative brain MRI into clinical decision support that radiologists and neurologists use at the point of care.
Background
I completed my MD at Mayo Clinic, where I helped build and scale an NLP-based patient portal message triage system called Domi (previously R00T) with optimizations for message routing in conjunction with COVID-19 emergency response teams. As an extension of The Care Collaboratory, I co-founded Positive Change Medical Students, which participated in healthcare design challenges with UC Berkeley's Innovation Group, including redesigning workflows and environments for long-stay hospital patients and addressing pandemic response through the Twindemic Design Challenge. As part of a dual degree, I earned an MS in Biomimicry at Arizona State University. Earlier research training includes immunology at the NIH under Sumati Rajagopalan and Eric O. Long, structural biology at Stockholm University with Ronnie Berntsson and Pål Stenmark, and biochemistry at the University of Illinois at Urbana-Champaign under Susan Martinis.
Born and raised in Chicagoland area
Based in Arizona 🏜️
What I'm Thinking About
"The hidden harmony is stronger than the visible."
- Responsible health AI — Safety frameworks for AI systems in clinical contexts: evaluation methodology, failure mode analysis, post-deployment monitoring, and the unsolved problem of building systems that inform without creating harm.
- Multimodal, ambient health sensing — Integrating voice, physiological signals, gait, sleep architecture, and facial micro-expressions from wearables and ambient sensors for continuous, passive health monitoring.
- Decentralized clinical trials — Remote monitoring and digital endpoints that bring trials to patients rather than patients to sites.
- Adaptive health interventions — Context-aware systems that deliver the right support at the right moment, using behavioral patterns and real-time data to personalize timing, framing, and intensity.
- Healthcare operations automation — Agentic systems that handle scheduling, prior authorizations, and clinical workflows end-to-end.
- Biological foundation models — Large-scale models trained on protein sequences, genomics, and molecular data to predict biological function.
- Theragnostic platforms — Photoacoustic and bioelectric systems that diagnose and treat simultaneously at the molecular level.
More in this talk.