305 Academic Research Building
265 South 37th Street
Philadelphia, PA 19104
Research Interests: Statistics and machine learning
Postdoctoral research fellow positions available
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.
Patrick Chao, Alexander Robey, Edgar Dobriban, Seyed Hamed Hassani, George J. Pappas, Eric Wong, Jailbreaking Black Box Large Language Models in Twenty Queries.
Behrad Moniri, Donghwan Lee, Seyed Hamed Hassani, Edgar Dobriban, A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks.
Zhixiang Zhang, Sokbae Lee, Edgar Dobriban A Framework for Statistical Inference via Randomized Algorithms.
Xinmeng Huang, Kan Xu, Donghwan Lee, Seyed Hamed Hassani, Hamsa Bastani, Edgar Dobriban Optimal Heterogeneous Collaborative Linear Regression and Contextual Bandits.
Tengyao Wang, Edgar Dobriban, Milana Gataric, Richard J. Samworth Sharp-SSL: Selective high-dimensional axis-aligned random projections for semi-supervised learning.
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.