SEEK2000
A local-first evidence synthesis engine that retrieves relevant evidence, preserves provenance, synthesizes supported answers, and makes uncertainty explicit.
Visit n61 →My work sits at the intersection of data, technology, research, and organizational decision-making.
Computational epidemiologist, data and AI leader, researcher, and founder of n61.Based in Anchorage, Alaska.
My current work through n61 focuses on practical AI products built around evidence, privacy, provenance, and human judgment.
A local-first evidence synthesis engine that retrieves relevant evidence, preserves provenance, synthesizes supported answers, and makes uncertainty explicit.
Visit n61 →Privacy-preserving synthetic population generation for research, planning, modeling, and decision support.
Visit n61 →Two decades of work integrating fragmented data across research, clinical, and operational environments to support real-world decision-making.
My background is in computational epidemiology and applied public health research. Across more than 100 scholarly and technical publications—including 42 peer-reviewed publications—my work has focused on extracting defensible conclusions from complex, imperfect data.
That same discipline now informs my work in data systems and AI: know where the evidence came from, distinguish what the data support from what they do not, and make uncertainty visible.
I started as an epidemiologist asking questions about populations and health. Increasingly, those questions became problems of data: how information is collected, connected, analyzed, interpreted, and ultimately used to make decisions.
That thread has taken me through academic research, healthcare, enterprise analytics, organizational leadership, and now the development of evidence-bounded AI systems through n61.
I am a Fellow of the American College of Epidemiology and hold a PhD in Epidemiology with a cognate in Biostatistics.