World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
46
Citations
10218
World Ranking
6771
National Ranking
2982

Overview

Sujay Sanghavi is affiliated with The University of Texas at Austin in the United States and focuses primarily on research within the field of Computer Science. Their work extensively covers subfields including Artificial Intelligence, Management Science and Operations Research, Computational Mechanics, Statistics and Probability, and Computer Vision and Pattern Recognition.

The main topics explored in their research include:

  • Stochastic Gradient Optimization Techniques
  • Sparse and Compressive Sensing Techniques
  • Machine Learning and Data Classification
  • Advanced Bandit Algorithms Research
  • Machine Learning and Algorithms
  • Topic Modeling
  • Privacy-Preserving Technologies in Data

Sanghavi has a significant publication record with 81 works predominantly appearing in two main venues: arXiv (Cornell University) with 50 publications and IEEE Transactions on Parallel and Distributed Systems with 1 publication.

Recent papers authored or co-authored by Sanghavi include:

  • "On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning" (2021), IEEE Transactions on Parallel and Distributed Systems
  • "Choosing the Sample with Lowest Loss makes SGD Robust" (2020), arXiv (Cornell University)
  • "Faster Non-Convex Federated Learning via Global and Local Momentum" (2020), arXiv (Cornell University)
  • "Robust Training in High Dimensions via Block Coordinate Geometric Median Descent" (2021), arXiv (Cornell University)
  • "DataComp-LM: In search of the next generation of training sets for language models" (2024), arXiv (Cornell University)

Frequent collaborators in Sanghavi's research include Inderjit S. Dhillon, Rudrajit Das, Anish Acharya, Tongzheng Ren, and Abolfazl Hashemi.

Best Publications

  • Rank-Sparsity Incoherence for Matrix Decomposition

    Venkat Chandrasekaran;Sujay Sanghavi;Pablo A. Parrilo;Alan S. Willsky

  • Low-rank matrix completion using alternating minimization

    Prateek Jain;Praneeth Netrapalli;Sujay Sanghavi

  • Robust PCA via Outlier Pursuit

    Huan Xu;C. Caramanis;S. Sanghavi

  • Phase Retrieval Using Alternating Minimization

    Praneeth Netrapalli;Prateek Jain;Sujay Sanghavi

  • A Dirty Model for Multi-task Learning

    Ali Jalali;Sujay Sanghavi;Chao Ruan;Pradeep K. Ravikumar

  • Sparse and low-rank matrix decompositions

    Venkat Chandrasekaran;Sujay Sanghavi;Pablo A. Parrilo;Alan S. Willsky

  • Gossiping with Multiple Messages

    S. Sanghavi;B. Hajek;L. Massoulie

  • Low-Rank Matrix Recovery From Errors and Erasures

    Yudong Chen;A. Jalali;S. Sanghavi;C. Caramanis

  • Learning the graph of epidemic cascades

    Praneeth Netrapalli;Sujay Sanghavi

  • Distributed link scheduling with constant overhead

    Sujay Sanghavi;Loc Bui;R. Srikant

  • Non-convex Robust PCA

    Praneeth Netrapalli;Niranjan U N;Sujay Sanghavi;Animashree Anandkumar

  • Clustering Sparse Graphs

    Yudong Chen;Sujay Sanghavi;Huan Xu

  • Sequential Compressed Sensing

    D.M. Malioutov;S.R. Sanghavi;A.S. Willsky

  • Clustering partially observed graphs via convex optimization

    Yudong Chen;Ali Jalali;Sujay Sanghavi;Huan Xu

  • Dropping Convexity for Faster Semi-definite Optimization

    Srinadh Bhojanapalli;Anastasios Kyrillidis;Sujay Sanghavi

  • Message Passing for Maximum Weight Independent Set

    S. Sanghavi;D. Shah;A.S. Willsky

  • Robust Matrix Completion and Corrupted Columns

    Yudong Chen;Huan Xu;Constantine Caramanis;Sujay Sanghavi

  • Coherent Matrix Completion

    Yudong Chen;Srinadh Bhojanapalli;Sujay Sanghavi;Rachel Ward

  • Learning with Bad Training Data via Iterative Trimmed Loss Minimization

    Yanyao Shen;Sujay Sanghavi

  • Coherent Matrix Completion

    Srinadh Bhojanapalli;Yudong Chen;Sujay Sanghavi;Rachel Ward

  • Non-square matrix sensing without spurious local minima via the Burer-Monteiro approach

    Dohyung Park;Anastasios Kyrillidis;Constantine Caramanis;Sujay Sanghavi

  • Improved Graph Clustering

    Yudong Chen;Sujay Sanghavi;Huan Xu

Frequent Co-Authors

Sanjay Shakkottai
Sanjay Shakkottai The University of Texas at Austin
Constantine Caramanis
Constantine Caramanis The University of Texas at Austin
Praneeth Netrapalli
Praneeth Netrapalli Google (United States)
Huan Xu
Huan Xu Alibaba Group (China)
Alexandros G. Dimakis
Alexandros G. Dimakis The University of Texas at Austin
Prateek Jain
Prateek Jain Google (United States)
Inderjit S. Dhillon
Inderjit S. Dhillon Google (United States)
Bruce Hajek
Bruce Hajek University of Illinois at Urbana-Champaign
Rachel Ward
Rachel Ward The University of Texas at Austin

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