Edgar Dobriban

The two main interests in my group are:

• the efficient analysis of “big data” using advanced tools, such as those from random matrix theory
  • PCA: [1], [2], [3], [4], [5]
  • multiple testing: [1], [2], [3]
  • high-dimensional regression: [1], [2]
• theoretical analysis of modern machine learning, including deep learning
  • data augmentation
  • weight normalization
  • stochastic gradient descent and flow
  • sketching and random projections, [1], [2], [3]
  • distributed learning: [1], [2]

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.
• I occasionally work with exceptional undergraduate students, both local ones and via summer internships. For this, typically a GPA of at least 3.8 is required (or a ranking of top 10% in your class), with strong mathematical, statistical and computational coursework. Update for 2020: Due to covid-19, only possibly online internships will be considered.

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:

• Mar 2020: New paper on stochastic gradient descent and flow with Alnur Ali and Ryan Tibshirani.
• Feb 2020: New paper on iterative sketching with Jonathan Lacotte, Sifan Liu, and Mert Pilanci.
• Feb 2020: Talk at UMass Amherst (cancelled due to unforeseen cicrumstances).
• Jan 2020: Edgar becomes an affiliate of the Warren Center.
• Dec 2019: Edgar will be teaching Continuing Education course “Machine Learning and Deep Learning,” with Annie Qu and Xiao Wang, at the 2020 Joint Statistical Meetings (JSM). Also teaching short course “Deep Learning in Statistics” at SLDS 2020.
• Dec 2019: Ridge regression paper with Sifan Liu accepted as a spotlight presentation to ICLR 2020 (top 6% of papers).
• Dec 2019: New GPU machines arrive.
• Nov 2019: New paper on Implicit Regularization of Normalization Methods
• Oct 2019: Reading group/group meeting starts.
• Sept-Nov 2019: Edgar is visiting the IAS for the Special year on Statistics, Machine Learning, and Optimization.
• Sept 2019: PCA paper accepted to the Annals of Statistics
• Sept 2019: Multiple testing paper accepted to Biometrika
• Sept 2019: Sketching paper accepted to NeurIPS 2019
• Aug 2019: Edgar contributes to ASA statement “Comment on Statistics, as a Discipline and Practice, in AI Research and Development“
• Aug 2019: David Hong joins the group. Welcome David!
• July 2019: NSF TRIPODS awarded to Edgar as a co-PI, with Alejandro Ribeiro (PI, ESE), and co-PIs Robert Ghrist (Math), Kostas Daniilidis (CIS), Saswati Sarkar (ESE)
• July 2019: New paper on data augmentation with Shuxiao Chen and Jane Lee.
• Summer 2019: Undergraduate researchers Tianle Liu (THU), Shuo Xie (PKU), and Weichen Zheng (Penn) work in the group
• June-Aug 2019: Edgar is attending the Summer program on the Foundations of Deep Learning at the Simons Institute.
• March 2019: New paper on distributed learning with Yue Sheng.

Miscellanea:

• 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…