World's Best Scientists 2026 revealed!
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Mathematics
USA
2026

D-Index & Metrics

Computer Science

D-Index
79
Citations
43441
World Ranking
1118
National Ranking
598

Mathematics

D-Index
77
Citations
40549
World Ranking
176
National Ranking
102

Research.com Recognitions

  • 2026 - Research.com Mathematics in United States Leader Award
  • 2025 - Research.com Mathematics in United States Leader Award
  • 2014 - Member of the National Academy of Sciences
  • 2013 - Fellow of the American Academy of Arts and Sciences
  • 2008 - Fellow of the American Association for the Advancement of Science (AAAS)
  • 2006 - Fellow of John Simon Guggenheim Memorial Foundation
  • 2005 - Fellow of the American Statistical Association (ASA)

Overview

Bin Yu is affiliated with the University of California, Berkeley, in the United States. Their research spans several domains with a strong focus on computer science and mathematics. Within these fields, their work covers subfields such as artificial intelligence, mathematical physics, computational theory and mathematics, molecular biology, and statistics and probability.

The scientist's research topics include:

  • Homotopy and Cohomology in Algebraic Topology
  • Algebraic structures and combinatorial models
  • Topological and Geometric Data Analysis
  • Bone Metabolism and Diseases
  • TGF-β signaling in diseases
  • S100 Proteins and Annexins
  • Advanced Computational Techniques and Applications

Bin Yu has contributed to multiple publications across various venues. Frequent publication outlets include arXiv (Cornell University) with five works, and journals such as the Journal of Orthopaedic Translation, IEEE Transactions on Instrumentation and Measurement, Proceedings of the National Academy of Sciences, and Econometrica.

Recent papers feature the following:

  • Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data, 2023, Econometrica
  • The CD163/TWEAK/Fn14 axis: A potential therapeutic target for alleviating inflammatory bone loss, 2024, Journal of Orthopaedic Translation
  • SCADA Data-Driven Spatio-Temporal Graph Convolutional Neural Network for Wind Turbine Fault Diagnosis, 2025, IEEE Transactions on Instrumentation and Measurement
  • Fast Interpretable Greedy-Tree Sums, 2025, Proceedings of the National Academy of Sciences
  • The Data Science Process: One Culture, 2020, International Statistical Review

Bin Yu has collaborated frequently with several coauthors, including Deqiang He, Jiachen Ma, Yang Fu, Zhihao Wang, and Jie Wu.

They have received multiple honors over the years including:

  • Member of the National Academy of Sciences, 2014
  • Fellow of the American Academy of Arts and Sciences, 2013
  • Fellow of the American Association for the Advancement of Science (AAAS), 2008
  • Fellow of John Simon Guggenheim Memorial Foundation, 2006
  • Fellow of the American Statistical Association (ASA), 2005

Best Publications

  • Adaptive wavelet thresholding for image denoising and compression

    S.G. Chang;Bin Yu;M. Vetterli

  • Spatially adaptive wavelet thresholding with context modeling for image denoising

    S.G. Chang;Bin Yu;M. Vetterli

  • On Model Selection Consistency of Lasso

    Peng Zhao;Bin Yu

  • Artificial intelligence and statistics

    Bin Yu;Karl Kumbier

  • Definitions, methods, and applications in interpretable machine learning.

    W. James Murdoch;Chandan Singh;Karl Kumbier;Reza Abbasi-Asl;Reza Abbasi-Asl

  • A Unified Framework for High-Dimensional Analysis of $M$-Estimators with Decomposable Regularizers

    Sahand N. Negahban;Pradeep Ravikumar;Martin J. Wainwright;Bin Yu

  • The minimum description length principle in coding and modeling

    A. Barron;J. Rissanen;Bin Yu

  • Spectral clustering and the high-dimensional stochastic blockmodel

    Karl Rohe;Sourav Chatterjee;Bin Yu

  • Reconstructing visual experiences from brain activity evoked by natural movies

    Shinji Nishimoto;An T. Vu;Thomas Naselaris;Yuval Benjamini

  • Boosting With the L2 Loss

    Peter Lukas Bühlmann;Bin Yu

  • LASSO-TYPE RECOVERY OF SPARSE REPRESENTATIONS FOR HIGH-DIMENSIONAL DATA

    Nicolai Meinshausen;Bin Yu

  • High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence

    Pradeep Ravikumar;Martin J. Wainwright;Garvesh Raskutti;Bin Yu

  • Model Selection and the Principle of Minimum Description Length

    Mark H Hansen;Bin Yu

  • Metalearners for estimating heterogeneous treatment effects using machine learning

    Sören R. Künzel;Jasjeet S. Sekhon;Peter J. Bickel;Bin Yu

  • Analyzing Bagging

    Unknown

  • The composite absolute penalties family for grouped and hierarchical variable selection

    Peng Zhao;Guilherme Rocha;Bin Yu

  • Internet tomography

    A. Coates;A.O. Hero;R. Nowak;Bin Yu

  • Network Tomography: Recent Developments

    Rui Castro;Mark Coates;Gang Liang;Robert Nowak

  • Minimax Rates of Estimation for High-Dimensional Linear Regression Over $ll_q$ -Balls

    G. Raskutti;M. J. Wainwright;Bin Yu

  • Boosting with early stopping: Convergence and consistency

    Tong Zhang;Bin Yu

  • Restricted Eigenvalue Properties for Correlated Gaussian Designs

    Garvesh Raskutti;Martin J. Wainwright;Bin Yu

  • Statistical guarantees for the EM algorithm: From population to sample-based analysis

    Sivaraman Balakrishnan;Martin J. Wainwright;Bin Yu

Frequent Co-Authors

Martin Vetterli
Martin Vetterli École Polytechnique Fédérale de Lausanne
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Pradeep Ravikumar
Pradeep Ravikumar Carnegie Mellon University
Peter J. Bickel
Peter J. Bickel University of California, Berkeley
Terence P. Speed
Terence P. Speed Walter and Eliza Hall Institute of Medical Research
Jorma Rissanen
Jorma Rissanen Helsinki Institute for Information Technology
Jack L. Gallant
Jack L. Gallant University of California, Berkeley
Susan E. Celniker
Susan E. Celniker Lawrence Berkeley National Laboratory

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