Yang Long

Welcome! I am a final-year PhD candidate in Statistics at George Mason University, advised by Dr. David Kepplinger and Dr. Lily Wang. My research focuses on trustworthy AI: robust, scalable statistical methods and theory for neuroimaging and multispectral/hyperspectral imaging, with valid uncertainty quantification under outliers, missingness, and surrogate-model misspecification.

Methodologically, I develop estimation and inference procedures that remain reliable under realistic imperfections—outliers, heavy-tailed noise, artifacts, measurement error, and missingness—while providing domain-wide uncertainty quantification, often via simultaneous confidence corridors for images and other functional targets. In related work, I explore generative models as auxiliary tools that preserve valid statistical inference. More broadly, my agenda is to build robust, scalable, and transparent statistical and AI pipelines for complex, structured imaging data.