I am currently a PhD student in Statistics at Duke University, supervised by David Dunson. I am also a graduate student in Applied Mathematics at Columbia University.
I am broadly interested in Probabilistic/Bayesian Machine Learning. In particular, I am working on incorporating mathematical/physical structure into various machine learning models. I am also interested in applications in Chemistry, Neuroscience, Medical Imaging, and Financial Econometrics.
Prior to Duke, I studied Computer Science at the University of Chicago, performing research at Booth’s Department of Econometrics and Statistics under the mentorship of Veronika Rockova.
Prior to Chicago, I studied Biomedical Engineering at Yale University, where I performed research on olfaction in Drosophila at the laboratory of John Carlson.
I was an undergraduate student at Johns Hopkins, majoring in Biomedical Engineering, Neuroscience as well as Applied Mathematics and Statistics.
At Hopkins, I did research on the molecular mechanisms of cortical development in mice at the laboratory of Alex Kolodkin. I have also worked on various research projects applying computational anatomy to analyze neurodegenerative diseases at the Center for Imaging Science with Michael Miller and Tilak Ratnanather.
In my free time, I enjoy reading Kung-Fu novels and writing iOS apps.
My CV is available upon request.
PhD in Statistical Science (Expected), 2022
MS in Computer Science, 2018
University of Chicago
MS in Biomedical Engineering, 2017
BS (Hons) in Biomedical Engineering, Neuroscience, and Applied Mathematics & Statistics, 2016
Johns Hopkins University
I have taught several graduate and undergraduate classes at various universities as a Teaching Assistant.