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):
Recent news:
Miscellanea:
Talk slides: GitHub. Google Scholar.
Edgar Dobriban and Zhanran Lin Joint Coverage Regions: Simultaneous Confidence and Prediction Sets.
Donghwan Lee, Behrad Moniri, Xinmeng Huang, Seyed Hamed Hassani, Edgar Dobriban (Work In Progress), Demystifying Disagreement-on-the-Line in High Dimensions.
Matteo Sesia, Stefano Favaro, Edgar Dobriban, Conformal Frequency Estimation with Sketched Data under Relaxed Exchangeability.
Hongxiang Qiu, Xu Shi, Wang Miao, Edgar Dobriban, Eric Tchetgen Tchetgen, Doubly Robust Proximal Synthetic Controls.
Description: https://arxiv.org/abs/2210.02014
Sangdon Park, Edgar Dobriban, Osbert Bastani, Insup Lee PAC Prediction Sets for Meta-Learning.
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.
Shuo Li, Xiayan Ji, Edgar Dobriban, Oleg Sokolsky, Insup Lee PAC-Wrap: Semi-Supervised PAC Anomaly Detection.
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 )
Study under the direction of a faculty member.
AMCS9999080
AMCS9990080
AMCS9950080
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.
AMCS5999080
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.