Research

My main research interest is in the mathematical foundations of data science, particularly the algorithmic primitives that drive modern data science and machine learning tasks. This involves statistics, high-dimensional probability, algorithms, optimization and sampling, and some algebra.

Publications

You can also find my articles on my Google Scholar profile.

Preprints

Perspectives on Stochastic Localization
BS, Kevin Tian, Matthew S. Zhang

Efficient Tensor Decomposition via Moment Matrix Extension
BS, Julia Lindberg, Joe Kileel

Journal Papers

Concentration Inequalities for Sums of Markov Dependent Random Matrices
Joe Neeman, BS, Rachel Ward
In Information and Inference

Generalization Bounds for Sparse Random Feature Expansions
Abolfazl Hashemi, Hayden Schaeffer, BS, Ufuk Topcu, Giang Tran, Rachel Ward
In Applied and Computational Harmonic Analysis

Conference Papers

SHRIMP: Sparser Random Feature Models via Iterative Magnitude Pruning
Yuege Xie, BS, Hayden Schaeffer, Rachel Ward
In Mathematical and Scientific Machine Learning 2022