One Question, Several Threads
My research follows a single question: how do we make quantitative evidence trustworthy enough to act on? I approach it on four fronts at once, from the analytic choices behind a result, to the security of the systems that produce it, to how findings are taught and translated into decisions. The methods work below is the core, and the same throughline runs out to regulatory practice, to the security of the AI tools now doing analysis, and to how the next generation is taught to judge evidence.
Researcher Degrees of Freedom
A single dataset supports many reasonable analyses. My dissertation builds methods for the choices analysts make: vibration-of-effects and specification analyses that trace how a result depends on those choices. It also asks a question analysts rarely get to answer, “when is an analysis done?”, and treats it as a formal stopping problem. This work is joint with Brian Caffo, Stephanie Hicks, Roger Peng, and Martin Lindquist.
Trustworthy & Reproducible Data Science for Health and Policy
Methods only matter if their uncertainty travels intact to the people who act on them. As a CERSI Scholar with the U.S. Food and Drug Administration and Johns Hopkins, I study how to report variability from multiple analytic choices in a way that supports regulatory decisions, and how to communicate analytic uncertainty to non-specialist audiences without losing rigor. This thread connects to my applied collaborations on the Global Burden of Disease Study and the National COVID Cohort Collaborative (N3C).
Security & Governance of AI for Health and Science
The analysis pipeline is increasingly run by AI systems, which introduces a new class of failures. Drawing on prior work as an information security analyst and data governance specialist, I study how to secure autonomously acting language models and the open-source dependencies they rely on, from prompt injection to data poisoning. I advise on AI safety and governance and have written three O’Reilly Media chapters across application and information security; the full list is on the publications page.
Statistics & Data Science Education
Teaching people to weigh evidence is the same problem as my methods work, viewed from the classroom. I design instruction and assessment for analytic judgment that hold up when students have AI assistants, work recognized by an American Statistical Association (ASA) Best Contributed Paper Award and a Johns Hopkins Teaching Academy Fellowship.
Grants & Fellowships
My current work is supported by a Fulbright research award, which funds my program at the Ludwig Maximilian University of Munich on the analytic choices behind robust results. Earlier, I held an open-source software grant from the R Consortium, part of the Linux Foundation, and undergraduate research fellowships at the University of California, Irvine. On larger collaborative projects, I’ve contributed as named personnel to federally funded research with the National Aeronautics and Space Administration and the National Science Foundation.
Where I Hold Roles
Beyond Munich and Johns Hopkins, I hold research roles that keep me close to large-scale applied work. I’m a Senior Biostatistician and Collaborator with the Institute for Health Metrics and Evaluation, home of the Global Burden of Disease Study, and I was a Team Science Fellow and Visiting Scholar at Stanford Medicine. I’m also a Fellow of the Open Science Center at the Ludwig Maximilian University of Munich and a Scholar in the Center of Excellence in Regulatory Science and Innovation with the U.S. Food and Drug Administration and Johns Hopkins.
Full publication list and metrics on the publications page, or browse Google Scholar.
Selected Recognition
A few of the honors I’ve been grateful to receive, across research, teaching, and early-career work.