About Me

I build statistical and machine learning methods that quantify how far a model can be trusted when the data are rare and the stakes are high, and I work with institutions and decision-makers on how to evaluate and deploy those models. I’m a Ph.D. student in the Department of Statistics & Data Science at Carnegie Mellon University, advised by Dr. Ann B. Lee in the STAMPS Research Center. I began in astrostatistics, where I worked rare-event statistics by studying solar flares and large-scale inference tasks with all-sky surveys; I maintain collaborations with the Harvard & Smithsonian Center for Astrophysics and the CHASC International Astrostatistics Collaboration, and previously conducted research at NASA Goddard Space Flight Center.

I am a National Science Foundation Graduate Research Fellow, a Block Center Fellow for AI and Society, and a Senior Quad Fellow. I graduated from New College of Florida with a B.A. in Statistics & Applied Mathematics in 2022 and an M.S. in Applied Data Science in 2024, and I am the founder of OutlierOrbit, an applied statistics and AI consulting company that helps organizations and decision-makers get reliable answers from their data and build systems that improve decision-making.

Research Interests

  • Trustworthy uncertainty quantification: confidence sets and calibration valid in the tails, under distribution shift, and at the scale of hundreds of millions of observations
  • Anomaly detection, rare and extreme events, and discovery: unusual points, sequences, and collections of events
  • Uncertainty for decisions, operational AI, and deployment: calibrated uncertainty turned into decision rules, monitoring, and evaluation protocols for systems in production
  • Applications: energy and critical infrastructure, space sciences and space weather, AI systems

Research Collaborations

  • STAMPS@CMU – Statistical Methods for the Physical Sciences Research Center
  • CHASC - International CHASC Astro-Statistics Center

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