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Computer Science

D-Index
36
Citations
4499
World Ranking
11374
National Ranking
4679

Research.com Recognitions

  • 2012 - Fellow of Alfred P. Sloan Foundation

Overview

Prasad Raghavendra is a researcher primarily affiliated with the University of California, Berkeley in the United States. Their research is situated within the broad field of Computer Science, with significant contributions across multiple subfields including Artificial Intelligence, Computational Theory and Mathematics, Computer Networks and Communications, Statistics and Probability, and Statistical and Nonlinear Physics.

Their main areas of work focus on complexity and algorithms in graphs, machine learning and algorithms, advanced graph theory research, complex network analysis techniques, Markov chains and Monte Carlo methods, graph labeling and dimension problems, as well as optimization and search problems.

Some recent publications by Prasad Raghavendra include the following papers:

  • Symmetries and Complexity (Invited Talk), 2021, arXiv (Cornell University)
  • Randomness Efficient Noise Stability and Generalized Small Bias Sets, 2020, Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • List Decodable Subspace Recovery, 2020, arXiv (Cornell University)
  • Approximating Rectangles by Juntas and Weakly Exponential Lower Bounds for LP Relaxations of CSPs, 2021, SIAM Journal on Computing
  • Matrix Discrepancy from Quantum Communication, 2021, arXiv (Cornell University)

The researcher has worked frequently with co-authors including Sidhanth Mohanty, Raghu Meka, David X. Wu, Jess Banks, and Jonah Brown-Cohen. Sidhanth Mohanty stands out as the most frequent collaborator with four joint publications.

Prasad Raghavendra's research contributions have been disseminated mainly through venues such as:

  • arXiv (Cornell University)
  • Leibniz-Zentrum für Informatik (Schloss Dagstuhl)
  • SIAM Journal on Computing
  • Zenodo (CERN European Organization for Nuclear Research)

In 2012, they were recognized as a Fellow of the Alfred P. Sloan Foundation, an award that indicates notable contributions to their scientific discipline.

Best Publications

  • Optimal algorithms and inapproximability results for every CSP

    Prasad Raghavendra

  • Graph expansion and the unique games conjecture

    Prasad Raghavendra;David Steurer

  • Hardness of Learning Halfspaces with Noise

    Venkatesan Guruswami;Prasad Raghavendra

  • Lower Bounds on the Size of Semidefinite Programming Relaxations

    James R. Lee;Prasad Raghavendra;David Steurer

  • Rounding Semidefinite Programming Hierarchies via Global Correlation.

    Boaz Barak;Prasad Raghavendra;David Steurer

  • Beating the Random Ordering is Hard: Inapproximability of Maximum Acyclic Subgraph

    V. Guruswami;R. Manokaran;P. Raghavendra

  • Reductions between Expansion Problems

    Prasad Raghavendra;David Steurer;Madhur Tulsiani

  • The Power of Sum-of-Squares for Detecting Hidden Structures

    Samuel B. Hopkins;Pravesh K. Kothari;Aaron Potechin;Prasad Raghavendra

  • Agnostic Learning of Monomials by Halfspaces Is Hard

    Vitaly Feldman;Venkatesan Guruswami;Prasad Raghavendra;Yi Wu

  • Approximating CSPs with global cardinality constraints using SDP hierarchies

    Prasad Raghavendra;Ning Tan

  • Approximate Constraint Satisfaction Requires Large LP Relaxations

    Siu On Chan;James R. Lee;Prasad Raghavendra;David Steurer

  • Integrality Gaps for Strong SDP Relaxations of UNIQUE GAMES

    Prasad Raghavendra;David Steurer

  • Beating the Random Ordering Is Hard: Every Ordering CSP Is Approximation Resistant

    Venkatesan Guruswami;Johan HÅstad;Rajsekar Manokaran;Prasad Raghavendra

  • Many sparse cuts via higher eigenvalues

    Anand Louis;Prasad Raghavendra;Prasad Tetali;Santosh Vempala

  • Sdp gaps and ugc hardness for multiway cut, 0-extension, and metric labeling

    Rajsekar Manokaran;Joseph (Seffi) Naor;Prasad Raghavendra;Roy Schwartz

  • How to Round Any CSP

    Prasad Raghavendra;David Steurer

  • Making the Long Code Shorter

    Boaz Barak;Parikshit Gopalan;Johan Hastad;Raghu Meka

  • Approximations for the isoperimetric and spectral profile of graphs and related parameters

    Prasad Raghavendra;David Steurer;Prasad Tetali

  • Approximating np-hard problems efficient algorithms and their limits

    Venkatesan Guruswami;Prasad Raghavendra

  • A Note on Yekhanin's Locally Decodable Codes

    Prasad Raghavendra

  • Strongly refuting random CSPs below the spectral threshold

    Prasad Raghavendra;Satish Rao;Tselil Schramm

Frequent Co-Authors

David Steurer
David Steurer ETH Zurich
Venkatesan Guruswami
Venkatesan Guruswami University of California, Berkeley
James R. Lee
James R. Lee University of Washington
Boaz Barak
Boaz Barak Harvard University
Luca Trevisan
Luca Trevisan Bocconi University
Johan Håstad
Johan Håstad Royal Institute of Technology
Santosh Vempala
Santosh Vempala Georgia Institute of Technology
Vitaly Feldman
Vitaly Feldman Apple (United States)
Prasad Tetali
Prasad Tetali Carnegie Mellon University
Ilias Diakonikolas
Ilias Diakonikolas University of Wisconsin–Madison

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