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: Statistics and machine learning


The two main interests in my group are:

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 both the Statistics department and the AMCS program, as it gives higher chances for admission.

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.

Education (cv):

  • PhD in Statistics, Stanford University, 2017.  Advisor: David Donoho
  • BA in Mathematics, 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, Peter Bickel, Dan-Virgil Voiculescu, etc…


Continue Reading


Talk slides: GitHubGoogle Scholar.


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 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.


Latest Research

Jonathan Lacotte, Sifan Liu, Edgar Dobriban, Mert Pilanci (2020), Limiting spectrum of randomized hadamard transform and optimal iterative sketching methods, Neural Information Processing Systems (NeurIPS) 2020.
All Research