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Research interests
My research focuses on the theoretical foundations of statistical problems in estimation, prediction, and inference. In many modern settings, classical methods may not be reliable due to high dimensionality, failure of model assumptions, or other challenges. I work on distribution-free inference methods such as conformal prediction, and on developing hardness results to establish what types of questions can or cannot be solved with distribution-free methods. I am also interested in multiple testing methods, in algorithmic stability, and shape-constrained inference. I also collaborate on modeling and optimization problems in image reconstruction for medical imaging.
Bio
I am a Professor in the Department of Statistics at the University of Chicago. Before starting at U of C, I was a NSF postdoctoral fellow during 2012-13 in the Department of Statistics at Stanford University, supervised by Emmanuel Candès. I received my PhD in Statistics at the University of Chicago in 2012, advised by Mathias Drton and Nati Srebro, and a MS in Mathematics at the University of Chicago in 2009. Prior to graduate school, I was a mathematics teacher at the Park School of Baltimore from 2005 to 2007, and received an ScB in Mathematics from Brown University in 2005.
Photo by Erielle Bakkum Photography.