Rajesh Jayaram
Rajesh is a Research Scientist at Google NYC, where he is part of the NYC Algorithms and Optimization team. He received his PhD in Computer Science from Carnegie Mellon University in 2021, where he was advised by David P. Woodruff. Prior to that, in 2017 he received his B.S. in Mathematics and Computer Science from Brown University. His research focuses primarily on sublinear algorithms, especially randomized sketching and streaming algorithms for problems in big-data. In general, he enjoys thinking about dimensionality reduction: namely, to what extent can we compress the significant components of an enormous, noisy data-set? His work also spans property testing, machine learning, and optimization.
He also has a personal homepage.
He also has a personal homepage.
Authored Publications
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HyperAttention: Large-scale Attention in Linear Time
Amin Karbasi
Amir Zandieh
Insu Han
David Woodruff
HyperAttention: Long-context Attention in Near-Linear Time (2024) (to appear)
Streaming Euclidean MST to a Constant Factor
Amit Levi
Erik Waingarten
Xi Chen
54rd Annual ACM Symposium on Theory of Computing (STOC'23) (2023)
Optimal Fully Dynamic k-Center Clustering for Adaptive and Oblivious Adversaries
Preview
Monika Henzinger
Andreas Wiese
Proceedings of the 2023 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)
New Streaming Algorithms for High Dimensional EMD and MST
Amit Levi
Erik Waingarten
Xi Chen
To be submitted to ACM Symposium on Theory of Computing (STOC) (2022)
Stars: Tera-Scale Graph Building for Clustering and Learning
Warren Schudy
NeurIPS 2022 (2022)
