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Computer Science

D-Index
37
Citations
9276
World Ranking
10507
National Ranking
4399

Overview

Glenn Fung is affiliated with American Family Insurance in the United States. Their research primarily focuses on computer science, with significant contributions in artificial intelligence, computer vision and pattern recognition, computational mechanics, computational theory and mathematics, and statistical and nonlinear physics.

The scientist's research encompasses a variety of topics including:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Text and Document Classification Technologies
  • Machine Learning and Data Classification
  • Insurance and Financial Risk Management
  • Anomaly Detection Techniques and Applications
  • Image and Object Detection Techniques

Among Glenn Fung's recent papers are the following works:

  • Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention (2021), published in arXiv (Cornell University)
  • Designing and Deploying Insurance Recommender Systems Using Machine Learning (2020), published in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
  • Task-Optimized Word Embeddings for Text Classification Representations (2020), published in Frontiers in Applied Mathematics and Statistics
  • Simplicial 2-Complex Convolutional Neural Nets (2020), published in arXiv (Cornell University)
  • Assessing Hail Risk for Property Insurers with a Dependent Marked Point Process (2021), published in Journal of the Royal Statistical Society Series A (Statistics in Society)

Frequent co-authors collaborating with Glenn Fung include:

  • Eric Bunch
  • Jeffery Kline
  • Teja Kanchinadam
  • Daniel J. Dickinson
  • Qian You

The scientist frequently publishes in venues such as:

  • arXiv (Cornell University)
  • Frontiers in Applied Mathematics and Statistics
  • Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
  • Journal of the Royal Statistical Society Series A (Statistics in Society)
  • Proceedings of the AAAI Conference on Artificial Intelligence

Glenn Fung's work addresses both foundational and applied aspects of machine learning, with applications extending to insurance and financial risk management. Their publications reflect an interdisciplinary approach, spanning technical innovations in natural language processing, classification technologies, and novel algorithms for image analysis and anomaly detection.

Best Publications

  • Multicategory Proximal Support Vector Machine Classifiers

    Glenn M. Fung;O. L. Mangasarian

  • Proximal support vector machine classifiers

    Glenn Fung;Olvi L. Mangasarian

  • Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches

    Mark Schmidt;Glenn Fung;Rómer Rosales

  • A Feature Selection Newton Method for Support Vector Machine Classification

    Glenn M. Fung;O. L. Mangasarian

  • Active Learning from Crowds

    Yan Yan;Glenn M. Fung;R mer Rosales;Jennifer G. Dy

  • Semi-superyised support vector machines for unlabeled data classification

    Glenn Fung;O. L. Mangasarian

  • Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention

    Yunyang Xiong;Zhanpeng Zeng;Rudrasis Chakraborty;Mingxing Tan

  • On the dangers of cross-validation. An experimental evaluation

    R. Bharat Rao;Glenn Fung

  • SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information

    J. Stoeckel;G. Fung

  • Knowledge-Based Support Vector Machine Classifiers

    Glenn M. Fung;Olvi L. Mangasarian;Jude W. Shavlik

  • Modeling annotator expertise: Learning when everybody knows a bit of something

    Yan Yan;Rómer Rosales;Glenn Fung;Mark W. Schmidt

  • Incremental Support Vector Machine Classification.

    Glenn Fung;Olvi L. Mangasarian

  • Rule extraction from linear support vector machines

    Glenn Fung;Sathyakama Sandilya;R. Bharat Rao

  • Learning from multiple annotators with varying expertise

    Yan Yan;Rómer Rosales;Glenn Fung;Ramanathan Subramanian

  • Systems and methods for automated diagnosis and decision support for breast imaging

    Sriram Krishnan;R. Bharat Rao;Murat Dundar;Glenn Fung

  • Structure learning in random fields for heart motion abnormality detection

    M. Schmidt;K. Murphy;G. Fung;R. Rosales

  • Finite Newton method for Lagrangian support vector machine classification

    Glenn Fung;Olvi L. Mangasarian

  • Multiple Instance Learning for Computer Aided Diagnosis

    Murat Dundar;Balaji Krishnapuram;R. B. Rao;Glenn M. Fung

  • Predicting readmission risk with institution-specific prediction models

    Shipeng Yu;Faisal Farooq;Alexander van Esbroeck;Glenn Fung

  • From Transformation-Based Dimensionality Reduction to Feature Selection

    Mahdokht Masaeli;Jennifer G. Dy;Glenn M. Fung

Frequent Co-Authors

Shipeng Yu
Shipeng Yu Pinterest
Olvi L. Mangasarian
Olvi L. Mangasarian University of Wisconsin–Madison
Jennifer G. Dy
Jennifer G. Dy Northeastern University
Jinbo Bi
Jinbo Bi University of Connecticut
Arun Krishnan
Arun Krishnan Microsoft (United States)
Mark Schmidt
Mark Schmidt University of British Columbia
Bradly G. Wouters
Bradly G. Wouters Princess Margaret Cancer Centre
Jude W. Shavlik
Jude W. Shavlik University of Wisconsin–Madison
Roberto Hornero
Roberto Hornero University of Valladolid
Fabian J. Theis
Fabian J. Theis Technical University of Munich

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