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Raghu Meka

Computational Complexity Theory Algorithmic Learning Theory Probabilistic Analysis Of Algorithms
University of California, Los Angeles Samueli Computer Science

About

I am a Professor in the CS department at UCLA. I am broadly interested in complexity theory, learning and probability theory. See my publications and talks for more details. I am part of UCLA's theory group. I am pleased to have received IEEE's W. Wallace McDowell Award for 2025.

Research Performance Summary

104
Total Papers
4380
Total Citations
37
H-Index
2008-2026
Active Research Span

First Recorded Paper

Simultaneous unsupervised learning of disparate clusterings

Year: 2008

Citations: 132

Venue: Statistical Analysis and Data Mining: The ASA Data Science Journal 1 (3

Latest Recorded Paper

Sparsifying Sums of Positive Semidefinite Matrices

Year: 2026

Citations: 0

Venue: Proceedings of the 2026 Annual ACM-SIAM Symposium on Discrete Algorithms

Last 10 Years Publication Activity

This timeline shows the professor's yearly publication activity.

2026
2
2025
7
2024
6
2023
10
2022
10
2021
7
2020
10
2019
4
2018
4
2017
4
2016
2

Publication Venues and Collaboration

Journal, Conference, and Book Publication Breakdown

Conference 50
Journal 8
Book 0

Top Coauthors

Meka Klivans Gopalan

Research Impact by Period

2025-2026

Period Stats
Papers 9
Citations 8
Avg. Citations / Paper 0.9
H-Index 2

2020-2024

Period Stats
Papers 43
Citations 854
Avg. Citations / Paper 19.9
H-Index 17

2015-2019

Period Stats
Papers 22
Citations 1436
Avg. Citations / Paper 65.3
H-Index 17

2010-2014

Period Stats
Papers 24
Citations 1576
Avg. Citations / Paper 65.7
H-Index 14

2005-2009

Period Stats
Papers 6
Citations 506
Avg. Citations / Paper 84.3
H-Index 6

Contact and Professional Links

Contact Information

raghum@cs.ucla.edu
3108257879

Detected Research Keywords

Leakage Resilient Secret Resilient Secret Sharing Sparse Linear Regression Approx Random 2020 Guaranteed Rank Minimization Rank Minimization Singular Minimization Singular Value Singular Value Projection Sum Squares Lower Squares Lower Bounds