World's Best Scientists 2026 revealed!
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Engineering and Technology
China
2026

D-Index & Metrics

Engineering and Technology

D-Index
78
Citations
36658
World Ranking
587
National Ranking
100

Research.com Recognitions

  • 2026 - Research.com Engineering and Technology in China Leader Award
  • 2025 - Research.com Engineering and Technology in China Leader Award
  • 2009 - Fellow of Alfred P. Sloan Foundation

Overview

Wotao Yin is affiliated with Alibaba Group (China) and conducts research primarily in the fields of Computer Science and Engineering. Their work spans multiple subfields including Artificial Intelligence, Computational Mechanics, Numerical Analysis, Computer Networks and Communications, and Computational Theory and Mathematics.

The scientist's research topics cover a variety of advanced technical areas. These include:

  • Stochastic Gradient Optimization Techniques
  • Sparse and Compressive Sensing Techniques
  • Advanced Optimization Algorithms Research
  • Privacy-Preserving Technologies in Data
  • Machine Learning and Algorithms
  • Advanced Bandit Algorithms Research
  • Image and Signal Denoising Methods

Wotao Yin has published extensively with a strong presence in leading venues. Frequent publication venues include:

  • arXiv (Cornell University)
  • IEEE Transactions on Signal Processing
  • Journal of Scientific Computing
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Transactions on Power Systems

The scientist has collaborated frequently with several co-authors, notably:

  • Ernest K. Ryu
  • Daniel McKenzie
  • Tianyi Chen
  • Yuejiao Sun
  • Howard Heaton

Among recent papers, key publications include:

  • FedPD: A Federated Learning Framework With Adaptivity to Non-IID Data, 2021, IEEE Transactions on Signal Processing
  • Learning to Optimize: A Primer and A Benchmark, 2021, arXiv (Cornell University)
  • VAFL: a Method of Vertical Asynchronous Federated Learning, 2020, arXiv (Cornell University)
  • FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data, 2020, arXiv (Cornell University)
  • Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization, 2020, IEEE Transactions on Signal Processing

Wotao Yin is also an author of the book Large-Scale Convex Optimization, published in 2022 by Cambridge University Press.

In recognition of their contributions, Wotao Yin was named a Fellow of the Alfred P. Sloan Foundation in 2009.

Best Publications

  • A New Alternating Minimization Algorithm for Total Variation Image Reconstruction

    Yilun Wang;Junfeng Yang;Wotao Yin;Yin Zhang

  • An Iterative Regularization Method for Total Variation-Based Image Restoration

    Stanley J. Osher;Martin Burger;Donald Goldfarb;Jinjun Xu

  • Bregman Iterative Algorithms for $ll_1$-Minimization with Applications to Compressed Sensing

    Wotao Yin;Stanley Osher;Donald Goldfarb;Jerome Darbon

  • Iteratively reweighted algorithms for compressive sensing

    R. Chartrand;Wotao Yin

  • EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization

    Wei Shi;Qing Ling;Gang Wu;Wotao Yin

  • Global Convergence of ADMM in Nonconvex Nonsmooth Optimization

    Yu Wang;Wotao Yin;Jinshan Zeng

  • A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion

    Yangyang Xu;Wotao Yin

  • Fixed-Point Continuation for $ll_1$-Minimization: Methodology and Convergence

    Elaine T. Hale;Wotao Yin;Yin Zhang

  • A feasible method for optimization with orthogonality constraints

    Zaiwen Wen;Wotao Yin

  • Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm

    Zaiwen Wen;Wotao Yin;Yin Zhang

  • On the Linear Convergence of the ADMM in Decentralized Consensus Optimization

    Wei Shi;Qing Ling;Kun Yuan;Gang Wu

  • On the Global and Linear Convergence of the Generalized Alternating Direction Method of Multipliers

    Wei Deng;Wotao Yin

  • An efficient augmented Lagrangian method with applications to total variation minimization

    Chengbo Li;Wotao Yin;Hong Jiang;Yin Zhang

  • A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data

    Junfeng Yang;Yin Zhang;Wotao Yin

  • On the Convergence of Decentralized Gradient Descent

    Kun Yuan;Qing Ling;Wotao Yin

  • Total variation models for variable lighting face recognition

    T. Chen;Wotao Yin;Xiang Sean Zhou;D. Comaniciu

  • A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration

    Junfeng Yang;Wotao Yin;Yin Zhang;Yilun Wang

  • Denoising Prior Driven Deep Neural Network for Image Restoration

    Weisheng Dong;Peiyao Wang;Wotao Yin;Guangming Shi

  • Parallel Multi-Block ADMM with o(1 / k) Convergence

    Wei Deng;Ming-Jun Lai;Zhimin Peng;Wotao Yin

  • Alternating direction augmented Lagrangian methods for semidefinite programming

    Zaiwen Wen;Donald Goldfarb;Wotao Yin

  • An efficient algorithm for compressed MR imaging using total variation and wavelets

    Shiqian Ma;Wotao Yin;Yin Zhang;A. Chakraborty

Frequent Co-Authors

Stanley Osher
Stanley Osher University of California, Los Angeles
Qing Ling
Qing Ling Sun Yat-sen University
Yin Zhang
Yin Zhang Chinese University of Hong Kong, Shenzhen
Donald Goldfarb
Donald Goldfarb Columbia University
Zhu Han
Zhu Han University of Houston
Zhangyang Wang
Zhangyang Wang The University of Texas at Austin
Steve B Jiang
Steve B Jiang The University of Texas Southwestern Medical Center
Richard G. Baraniuk
Richard G. Baraniuk Rice University
Ali H. Sayed
Ali H. Sayed École Polytechnique Fédérale de Lausanne
Suhas Diggavi
Suhas Diggavi University of California, Los Angeles

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