New seminar class in Fall 2019: Topics in Deep Learning (STAT-991), surveying advanced topics in deep learning research based on student presentations. See the Github page for the class materials from Fall 2018 and Spring 2019.
PhD in Statistics, Stanford University, 2017. Advisor: David Donoho
BA in Mathematics, Princeton University, 2012.
I am actively looking for motivated PhD students. If you are already at Penn, please feel free to contact me about possible research projects.
For PhD applicants: if you are interested to work with me, please mention this on your application to the PhD program. Please apply through both the Statistics department and the AMCS program, as it gives higher chances for admission.
I am also looking for collaborators, both in statistics and in other areas. Please feel free to contact me about potential problems of mutual interest.
This seminar will be taken by doctoral candidates after the completion of most of their coursework. Topics vary from year to year and are chosen from advance probability, statistical inference, robust methods, and decision theory with principal emphasis on applications.
T.W. Anderson Theory of Statistics Dissertation Award, Department of Statistics, Stanford University, 2017
Howard Hughes Medical Institute International Student Graduate Research Fellowship, 2015
Stanford Department of Statistics Teaching Award, 2013
Middleton Miller ’29 Prize for best independent work in mathematics, Princeton University, 2012
This page has links to methods from my papers. Feel free to contact me if you are interested to use them.
P-value weighting techniques for multiple hypothesis testing. These can improve power in multiple testing, if there is prior information about the individual effect sizes. Includes the iGWAS method for Genome-Wide Association Studies.