Physician fascinated by how biology signals across scales, from quantum effects in living systems up through molecular, physiological, and behavioral layers. I work on the technology that reads those signals and on the systems that decide what matters for you in the moment.
Latest work: The Long Listen
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 clinical AI: evaluation methodology, failure mode analysis, post-deployment monitoring. The hardest unsolved piece is building systems that inform without harming. Looking for collaborators on rubric-based eval design.
- Multimodal, ambient health sensing. Voice, gait, sleep architecture, physiology, facial micro-expressions. The signal is there. The question is which combinations actually predict clinical outcomes versus which ones just correlate well in a paper.
- Decentralized clinical trials. Remote monitoring and digital endpoints. The economics now favor bringing trials to patients, but the regulatory and operational tooling is still catching up. What's the next bottleneck?
- Adaptive health interventions. Context-aware systems that deliver the right support at the right moment. The hard part isn't the model. It's the behavioral science, and most teams underweight it.
- Biological foundation models. Protein, genomics, molecular data. Curious how soon we get to multimodal models that span scales, from sequence to cell to tissue to phenotype.
- Theragnostic platforms. Photoacoustic and bioelectric systems that diagnose and treat in the same intervention. This is where biomimicry and quantum biology start to matter practically rather than philosophically.
More in this talk.
Connect
Are you building or validating health AI, wearables, or ambient sensing and want to chat? Schedule a meeting here.