Research
Broadly, I am interested in designing efficient algorithms with provable guarantees for problems in data science and machine learning.
More specifically, I have worked on sketching and coreset methods for problems in randomized numerical linear algebra, optimization and machine learning.

Projective Clustering Product Quantization
Aditya Krishnan, Edo Liberty
In Submission, 2021

Sublinear Time Spectral Density Estimation
Vladimir Braverman, Aditya Krishnan, Christopher Musco
ACM Symposium on Theory of Computing (STOC), 2022

Lifelong Learning with Sketched Structural Regularization
Haoran Li, Aditya Krishnan, Jingfeng Wu, Soheil Kolouri, Praveen K. Pilly, Vladimir Braverman
Asian Conference in Machine Learning (ACML), 2021

NearOptimal Entrywise Sampling of Numerically Sparse Matrices
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Shay Sapir
Conference on Learning Theory (COLT), PMLR, 2021

Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension
Vladimir Braverman, Robert Krauthgamer, Aditya Krishnan, Roi Sinoff
International Conference on Machine Learning (ICML), 2020

Competitively Pricing Parking in a Tree
Max Bender, Jacob Gilbert, Aditya Krishnan, Kirk Pruhs
Conference on Web and Internet Economics (WINE), 2020

On Sketching the q to p Norms
Aditya Krishnan, Sidhanth Mohanty, David P. Woodruff
International Conference on Approximation Algorithms for Combinatorial Optimization Problems (APPROX), 2018


Teaching Assistant, 601.433/601.633 Introduction to Algorithms: F19, S20, S22
Teaching Assistant, 601.435/601.635 Approximation Algorithms: S21
Coorganizer, JHU CS Theory Seminar, F21, S22

