World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
93
Citations
44980
World Ranking
508
National Ranking
271

Research.com Recognitions

  • 2016 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2014 - ACM Fellow For contributions to large-scale data analysis, machine learning and computational mathematics.
  • 2014 - SIAM Fellow For contributions to numerical linear algebra, data analysis, and machine learning.

Overview

Inderjit S. Dhillon is affiliated with The University of Texas at Austin in the United States. Their research primarily focuses on computer science with a significant emphasis on artificial intelligence. Other notable subfields include computer vision and pattern recognition, computer networks and communications, information systems, and management science and operations research.

The scientist has contributed to multiple research topics within their field. Key areas of work include:

  • Text and Document Classification Technologies
  • Topic Modeling
  • Stochastic Gradient Optimization Techniques
  • Domain Adaptation and Few-Shot Learning
  • Machine Learning and Data Classification
  • Machine Learning and Algorithms
  • Natural Language Processing Techniques

Among recent publications, several papers highlight current research directions:

  • Large-scale multi-label learning with missing labels, 2025, arXiv (Cornell University)
  • CAT: Customized Adversarial Training for Improved Robustness, 2022, Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
  • Learning to Encode Position for Transformer with Continuous Dynamical Model, 2020, arXiv (Cornell University)
  • Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification, 2021, arXiv (Cornell University)
  • On the Benefits of Multiple Gossip Steps in Communication-Constrained Decentralized Federated Learning, 2021, IEEE Transactions on Parallel and Distributed Systems

Research output is frequently published in notable venues such as:

  • arXiv (Cornell University)
  • Journal of Thoracic Oncology
  • Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
  • IEEE Transactions on Parallel and Distributed Systems
  • Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Frequent collaborators include:

  • Hsiang-Fu Yu
  • Cho-Jui Hsieh
  • Sujay Sanghavi
  • Wei-Cheng Chang
  • Lexing Ying

Inderjit S. Dhillon has been recognized with several awards including:

  • Fellow of the American Association for the Advancement of Science (AAAS), 2016
  • SIAM Fellow, 2014, for contributions to numerical linear algebra, data analysis, and machine learning
  • ACM Fellow, 2014, for contributions to large-scale data analysis, machine learning, and computational mathematics

Best Publications

  • Information-theoretic metric learning

    Jason V. Davis;Brian Kulis;Prateek Jain;Suvrit Sra

  • Co-clustering documents and words using bipartite spectral graph partitioning

    Inderjit S. Dhillon

  • ScaLAPACK Users' Guide

    L. S. Blackford;J. Choi;A. Cleary;E. D'Azevedo

  • ScaLAPACK user's guide

    L. S. Blackford;J. Choi;A. Cleary;E. D'Azeuedo

  • Clustering with Bregman Divergences

    Arindam Banerjee;Srujana Merugu;Inderjit S. Dhillon;Joydeep Ghosh

  • Concept Decompositions for Large Sparse Text Data Using Clustering

    Inderjit S. Dhillon;Dharmendra S. Modha

  • Information-theoretic co-clustering

    Inderjit S. Dhillon;Subramanyam Mallela;Dharmendra S. Modha

  • Kernel k-means: spectral clustering and normalized cuts

    Inderjit S. Dhillon;Yuqiang Guan;Brian Kulis

  • Weighted Graph Cuts without Eigenvectors A Multilevel Approach

    I.S. Dhillon;Yuqiang Guan;B. Kulis

  • A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation

    Arindam Banerjee;Inderjit Dhillon;Joydeep Ghosh;Srujana Merugu

  • Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

    Arindam Banerjee;Inderjit S. Dhillon;Joydeep Ghosh;Suvrit Sra

  • Semi-supervised graph clustering: a kernel approach

    Brian Kulis;Sugato Basu;Inderjit Dhillon;Raymond Mooney

  • Learning with Noisy Labels

    Nagarajan Natarajan;Inderjit S Dhillon;Pradeep K Ravikumar;Ambuj Tewari

  • A divisive information theoretic feature clustering algorithm for text classification

    Inderjit S. Dhillon;Subramanyam Mallela;Rahul Kumar

  • A Data-Clustering Algorithm on Distributed Memory Multiprocessors

    Inderjit S. Dhillon;Dharmendra S. Modha

  • Designing structured tight frames via an alternating projection method

    J.A. Tropp;I.S. Dhillon;R.W. Heath;T. Strohmer

  • Generalized Nonnegative Matrix Approximations with Bregman Divergences

    Suvrit Sra;Inderjit S. Dhillon

  • Towards Fast Computation of Certified Robustness for ReLU Networks

    Tsui-Wei Weng;Huan Zhang;Hongge Chen;Zhao Song

  • Large-scale Multi-label Learning with Missing Labels

    Hsiang-Fu Yu;Prateek Jain;Purushottam Kar;Inderjit Dhillon

  • Minimum sum-squared residue co-clustering of gene expression data

    Hyuk Cho;Inderjit S. Dhillon;Yuqiang Guan;Suvrit Sra

  • Guaranteed Rank Minimization via Singular Value Projection

    Prateek Jain;Raghu Meka;Inderjit S. Dhillon

  • ScaLAPACK: A Portable Linear Algebra Library for Distributed Memory Computers - Design Issues and Performance

    Laura Susan Blackford;J. Choi;A. Cleary;A. Petitet

Frequent Co-Authors

Cho-Jui Hsieh
Cho-Jui Hsieh University of California, Los Angeles
Pradeep Ravikumar
Pradeep Ravikumar Carnegie Mellon University
Prateek Jain
Prateek Jain Google (United States)
Brian Kulis
Brian Kulis Boston University
Ambuj Tewari
Ambuj Tewari University of Michigan–Ann Arbor
Joel A. Tropp
Joel A. Tropp California Institute of Technology
Arindam Banerjee
Arindam Banerjee University of Illinois at Urbana-Champaign
Zhao Song
Zhao Song Adobe Systems (United States)
James Demmel
James Demmel University of California, Berkeley

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