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, Deep Learning, Multiple testing

Overview

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.

Education (cv):

  • 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.

For PhD applicants: if you are interested to work with me, please mention this on your application to the PhD program.

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.

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Research

Talk slides: GitHub.  Google Scholar.

Teaching

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

Miscellaneous

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.

Activity

Latest Research

Shuxiao Chen, Edgar Dobriban, Jane H Lee Invariance reduces Variance: Understanding Data Augmentation in Deep Learning and Beyond.
All Research

Awards and Honors

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