Edgar Dobriban

Edgar Dobriban
  • Assistant Professor of Statistics and Data Science, with secondary appointment in Computer and Information Science

Contact Information

  • office Address:

    305 Academic Research Building
    265 South 37th Street
    Philadelphia, PA 19104

Research Interests: Statistics and machine learning


Postdoctoral research fellow positions available:

Research interests:

The group is always looking to expand. We are recruiting PhD students at Penn to work on problems in statistics and machine learning. PhD applicants interested to work with me should mention this on their application. Please apply through the departments of Statistics & Data Science, Computer and Information Science, and the AMCS program, as it gives higher chances for admission.

Education (cv):

  • PhD in Statistics, Stanford University, 2017.  Advisor: David Donoho
  • BA in Mathematics (with highest honors/summa cum laude), Princeton University, 2012.

Recent news:


  • I use Twitter to keep up with new research.
  • I grew up in Romania, and speak Hungarian as a first language (the real spelling of my name is Dobribán Edgár). These two countries are and were the origin of many great mathematicians and statisticians, including John von Neumann, Abraham Wald, Paul Erdos, Dan-Virgil Voiculescu, etc…


Continue Reading


Talk slides: GitHubGoogle Scholar.


Current Courses (Spring 2023)

  • STAT4310 - Statistical Inference

    Graphical displays; one- and two-sample confidence intervals; one- and two-sample hypothesis tests; one- and two-way ANOVA; simple and multiple linear least-squares regression; nonlinear regression; variable selection; logistic regression; categorical data analysis; goodness-of-fit tests. A methodology course. This course does not have business applications but has significant overlap with STAT 1010 and 1020. This course may be taken concurrently with the prerequisite with instructor permission.

    STAT4310001 ( Syllabus )

    STAT4310002 ( Syllabus )

  • AMCS9999 - Ind Study & Research

    Study under the direction of a faculty member.


  • AMCS9990 - Masters Reg Tuition


  • AMCS9950 - Dissertation


  • AMCS5999 - Independent Study

    Independent Study allows students to pursue academic interests not available in regularly offered courses. Students must consult with their academic advisor to formulate a project directly related to the student’s research interests. All independent study courses are subject to the approval of the AMCS Graduate Group Chair.


Awards and Honors

  • Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award 2023, 2023
  • Sloan Research Fellowship in Mathematics, 2023
  • Bernoulli Society New Researcher Award 2023, 2023
  • Junior research award, ICSA 2022 China Conference, 2022
  • NSF CAREER Award, 2021
  • NSF-Simons Award on Mathematical and Scientific Foundations of Deep Learning, co-PI, 2020
  • NSF Harnessing the Data Revolution TRIPODS Award, co-PI, 2019
  • 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.

ePCA: github

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

EigenEdge: github

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.

pweight : github R. github Matlab

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.