305 Academic Research Building
265 South 37th Street
Philadelphia, PA 19104
Research Interests: Statistics and machine learning
A postdoctoral research fellow position is available to work on adaptive and adversarial machine learning, with connections to developmental psychology. Please see ad and send e-mail to Edgar.
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.
Matteo Sesia, Stefano Favaro, Edgar Dobriban, Conformal Frequency Estimation with Sketched Data under Relaxed Exchangeability.
Druv Pai, Michael Psenka, Chih-Yuan Chiu, Manxi Wu, Edgar Dobriban, Yi Ma Pursuit of a Discriminative Representation for Multiple Subspaces via Sequential Games.
Yinshuang Xu, Jiahui Lei, Edgar Dobriban, Kostas Daniilidis (2022), Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces,.
Souradeep Dutta, Yahan Yang, Elena Bernardis, Edgar Dobriban, Insup Lee, Memory Classifiers: Two-stage Classification for Robustness in Machine Learning.
Xinmeng Huang, Donghwan Lee, Edgar Dobriban, Seyed Hamed Hassani, Collaborative Learning of Distributions under Heterogeneity and Communication Constraints.
Xianli Zeng, Edgar Dobriban, Guang Cheng Fair Bayes-Optimal Classifiers Under Predictive Parity.
Evangelos Chatzipantazis, Stefanos Pertigkiozoglou, Edgar Dobriban, Kostas Daniilidis, SE(3)-Equivariant Attention Networks for Shape Reconstruction in Function Space.
This page has links to methods from my papers. Feel free to contact me if you are interested to use them.
The ePCA method for principal component analysis of exponential family data, e.g. Poisson-modeled count data. (with L.T. Liu);
Methods for working with large random data matrices, including
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.