Edgar Dobriban

Edgar Dobriban
  • Assistant Professor of Statistics

Contact Information

  • office Address:

    465 Jon M. Huntsman Hall
    3730 Walnut Street
    Philadelphia, PA 19104

Research Interests: Statistical methods for big data, High-dimensional asymptotics, Random matrix theory, Multiple testing



  • PhD in Statistics, Stanford University, 2017.  Advisor: David Donoho
  • BA in Mathematics, Princeton University, 2012.

I am actively looking for motivated students. If you are already at Penn, please feel free to contact me about possible research projects.

Current (co-)advisees: Yue Sheng (AMCS), Sifan Liu (Tsinghua).

New class in Fall 2018: Topics in Deep Learning (STAT-991). See here for the class time and location.


Continue Reading


Talk slides: GitHub.


Current Courses

  • STAT430 - Probability

    Discrete and continuous sample spaces and probability; random variables, distributions, independence; expectation and generating functions; Markov chains and recurrence theory.



  • STAT991 - Sem In Adv Appl Of Stat

    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.


Awards and Honors

  • 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 this software.

ePCA: github

Contains the ePCA method for principal component analysis of exponential family data, e.g. Poisson-modeled count data. (with L.T. Liu, Liu et al., 2016);

EigenEdge: github

Contains methods for working with large random data matrices, including

  • Computing eigenvalue distributions of covariance matrices (general Marchenko-Pastur distributions).
  • Optimal statistics for testing in principal component analysis.
  • Tools for spiked covariance models: spike and cosine descriptors, optimal shrinkers.

Related papers: Dobriban, 2015Dobriban, 2016;

pweight : CRAN. github R. github Matlab

Implements P-value weighting techniques for multiple hypothesis testing. These methods can improve power in multiple testing, if there is prior information about the individual effect sizes. Includes the iGWAS method designed for Genome-Wide Association Studies.

Related papers: Dobriban et al., 2015; Fortney et al., 2015; Dobriban, 2016;


Awards and Honors

T.W. Anderson Theory of Statistics Dissertation Award, Department of Statistics, Stanford University 2017
All Awards