World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
74
Citations
25651
World Ranking
1471
National Ranking
765

Overview

Michael W. Mahoney is affiliated with the University of California, Berkeley in the United States. Their research primarily concerns the domain of Computer Science, with a focus on Artificial Intelligence. Their scholarship extends into related subfields including Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Computational Mechanics, and Signal Processing.

The extent of Mahoney's published work includes 284 contributions within Computer Science, with 199 specifically in Artificial Intelligence, 45 in Computer Vision and Pattern Recognition, 33 in Statistical and Nonlinear Physics, 30 in Computational Mechanics, and 17 in Signal Processing.

Topics frequently addressed in their research include:

  • Stochastic Gradient Optimization Techniques
  • Neural Networks and Applications
  • Sparse and Compressive Sensing Techniques
  • Model Reduction and Neural Networks
  • Advanced Neural Network Applications
  • Topic Modeling
  • Domain Adaptation and Few-Shot Learning

Mahoney has contributed papers to multiple publication venues, notably:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Nature Communications
  • INFORMS Journal on Optimization
  • SIAM Journal on Matrix Analysis and Applications

Some of the recent papers associated with Mahoney's research include:

  • "Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT" (2020), Proceedings of the AAAI Conference on Artificial Intelligence
  • "Characterizing possible failure modes in physics-informed neural networks" (2021), arXiv (Cornell University)
  • "Shallow neural networks for fluid flow reconstruction with limited sensors" (2020), Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
  • "ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning" (2021), Proceedings of the AAAI Conference on Artificial Intelligence
  • "AI and Memory Wall" (2024), IEEE Micro

Frequent collaborators in their academic work include Kurt Keutzer, Amir Gholami, N. Benjamin Erichson, Zhewei Yao, and Liam Hodgkinson. The collaborations with these coauthors indicate ongoing engagement with several experts in the fields of machine learning and computational science.

Best Publications

  • Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters

    Jure Leskovec;Kevin J. Lang;Anirban Dasgupta;Michael W. Mahoney

  • Empirical comparison of algorithms for network community detection

    Jure Leskovec;Kevin J. Lang;Michael Mahoney

  • Statistical properties of community structure in large social and information networks

    Jure Leskovec;Kevin J. Lang;Anirban Dasgupta;Michael W. Mahoney

  • On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning

    Petros Drineas;Michael W. Mahoney

  • CUR matrix decompositions for improved data analysis

    Michael W. Mahoney;Petros Drineas

  • A Survey of Quantization Methods for Efficient Neural Network Inference

    Amir Gholami;Sehoon Kim;Zhen Dong;Zhewei Yao

  • Randomized Algorithms for Matrices and Data

    Michael W. Mahoney

  • Fast Monte Carlo Algorithms for Matrices II: Computing a Low-Rank Approximation to a Matrix

    Petros Drineas;Ravi Kannan;Michael W. Mahoney

  • Relative-Error $CUR$ Matrix Decompositions

    Petros Drineas;Michael W. Mahoney;S. Muthukrishnan

  • Fast Monte Carlo Algorithms for Matrices I: Approximating Matrix Multiplication

    Petros Drineas;Ravi Kannan;Michael W. Mahoney

  • Faster least squares approximation

    Petros Drineas;Michael W. Mahoney;S. Muthukrishnan;Tamás Sarlós

  • Fast approximation of matrix coherence and statistical leverage

    Petros Drineas;Malik Magdon-Ismail;Michael W. Mahoney;David P. Woodruff

  • Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT

    Sheng Shen;Zhen Dong;Jiayu Ye;Linjian Ma

  • HAWQ: Hessian AWare Quantization of Neural Networks With Mixed-Precision

    Zhen Dong;Zhewei Yao;Amir Gholami;Michael Mahoney

  • An improved approximation algorithm for the column subset selection problem

    Christos Boutsidis;Michael W. Mahoney;Petros Drineas

  • Fast Monte Carlo Algorithms for Matrices III: Computing a Compressed Approximate Matrix Decomposition

    Petros Drineas;Ravi Kannan;Michael W. Mahoney

  • ZeroQ: A Novel Zero Shot Quantization Framework

    Yaohui Cai;Zhewei Yao;Zhen Dong;Amir Gholami

  • Revisiting the Nyström method for improved large-scale machine learning

    Alex Gittens;Michael W. Mahoney

  • PCA-correlated SNPs for structure identification in worldwide human populations.

    Peristera Paschou;Elad Ziv;Esteban G Burchard;Shweta Choudhry

  • Sampling algorithms for l2 regression and applications

    Petros Drineas;Michael W. Mahoney;S. Muthukrishnan

  • Sampling algorithms for l 2 regression and applications

    Petros Drineas;Michael W. Mahoney;S. Muthukrishnan

  • Fast approximation of matrix coherence and statistical leverage

    Petros Drineas;Malik Magdon-ismail;David Woodruff;Michael W. Mahoney

Frequent Co-Authors

Petros Drineas
Petros Drineas Purdue University West Lafayette
Kurt Keutzer
Kurt Keutzer University of California, Berkeley
David F. Gleich
David F. Gleich Purdue University West Lafayette
Ravi Kannan
Ravi Kannan Microsoft (United States)
David P. Woodruff
David P. Woodruff Carnegie Mellon University
Joseph E. Gonzalez
Joseph E. Gonzalez University of California, Berkeley
James Demmel
James Demmel University of California, Berkeley
Ananth Grama
Ananth Grama Purdue University West Lafayette
Malik Magdon-Ismail
Malik Magdon-Ismail Rensselaer Polytechnic Institute
Subbaratnam Muthukrishnan
Subbaratnam Muthukrishnan Kansas State University

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