World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
58
Citations
11070
World Ranking
2533
National Ranking
774

Overview

George C. Runger is affiliated with Arizona State University in the United States. Their primary area of research falls within the field of Computer Science, with a significant focus on Artificial Intelligence. Their work spans multiple intersecting subfields including General Health Professions, Industrial and Manufacturing Engineering, Food Science, and Molecular Biology.

The scientist's research encompasses a range of main topics, notably Machine Learning and Algorithms, Scheduling and Optimization Algorithms, and Gaussian Processes and Bayesian Inference. Additionally, they have contributed to the study of Food Supply Chain Traceability, Food Waste Reduction and Sustainability, Health Disparities and Outcomes, and Food Security and Health in Diverse Populations.

Some of George C. Runger's recent papers are:

  • "Dynamic incorporation of prior knowledge from multiple domains in biomarker discovery," 2020, BMC Bioinformatics
  • "Attention-Based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem," 2024, SSRN Electronic Journal
  • "On active learning methods for manifold data," 2020, Test
  • "Layered Market Intelligence System," 2022, Transportation research procedia
  • "Building an intelligent system to identify trends in agricultural markets," 2023, Journal of Cleaner Production

Frequent venues for publication include Test, arXiv (Cornell University), BMC Bioinformatics, SSRN Electronic Journal, and Transportation research procedia.

George C. Runger collaborates regularly with several coauthors, including Li Liu, Kailey Love, Seho Kee, Mani Janakiram, and Enrique Del Castillo, with multiple joint publications shared with each.

Best Publications

  • A time series forest for classification and feature extraction

    Houtao Deng;George Runger;Eugene Tuv;Martyanov Vladimir

  • Applied Statistics and Probability for Engineers.

    Unknown

  • A Bag-of-Features Framework to Classify Time Series

    M. G. Baydogan;G. Runger;E. Tuv

  • Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination

    Eugene Tuv;Alexander Borisov;George Runger;Kari Torkkola

  • Robustness of the EWMA Control Chart to Non-Normality

    Connie M. Borror;Douglas C. Montgomery;George C. Runger

  • A Combined Adaptive Sample Size and Sampling Interval X Control Scheme

    Sharad S. Prabhu;Douglas C. Montgomery;George C. Runger

  • Designing a Multivariate EWMA Control Chart

    Sharad S. Prabhu;George C. Runger

  • Using Experimental Design to Find Effective Parameter Settings for Heuristics

    Steven P. Coy;Bruce L. Golden;George C. Runger;Edward A. Wasil

  • Gene selection with guided regularized random forest

    Houtao Deng;George Runger

  • Integrating Statistical Process Control and Engineering Process Control

    Douglas C. Montgomery;J. Bert Keats;George C. Runger;William S. Messina

  • GAUGE CAPABILITY AND DESIGNED EXPERIMENTS. PART I: BASIC METHODS

    Douglas C. Montgomery;George C. Runger

  • A Markov Chain Model for the Multivariate Exponentially Weighted Moving Averages Control Chart

    George C. Runger;Sharad S. Prabhu

  • Comparison of multivariate CUSUM charts

    Joseph J. Pignatiello;George C. Runger

  • Time series representation and similarity based on local autopatterns

    Mustafa Gokce Baydogan;George Runger

  • Learning a symbolic representation for multivariate time series classification

    Mustafa Gokce Baydogan;George Runger

  • Optimization Problems and Methods in Quality Control and Improvement

    W. Matthew Carlyle;Douglas C. Montgomery;George C. Runger

  • Bias of importance measures for multi-valued attributes and solutions

    Houtao Deng;George Runger;Eugene Tuv

  • ADAPTIVE SAMPLING ENHANCEMENTS FOR SHEWHART CONTROL CHARTS

    George C. Runger;Douglas C. Montgomery

  • GAUGE CAPABILITY ANALYSIS AND DESIGNED EXPERIMENTS. PART II: EXPERIMENTAL DESIGN MODELS AND VARIANCE COMPONENT ESTIMATION

    Douglas C. Montgomery;George C. Runger

  • Process monitoring for multiple count data using generalized linear model-based control charts

    Katina R. Skinner;Douglas C. Montgomery;George C. Runger

  • Feature selection via regularized trees

    Houtao Deng;George Runger

  • Adaptative sampling for process control

    George C. Runger;Joseph J. Pignatiello

Frequent Co-Authors

Douglas C. Montgomery
Douglas C. Montgomery Arizona State University
John W. Fowler
John W. Fowler Arizona State University
Enrique Castillo
Enrique Castillo University of Cantabria
Weiwen Zhang
Weiwen Zhang Zhejiang University
Bruce L. Golden
Bruce L. Golden University of Maryland, College Park
Edward Wasil
Edward Wasil American University
Jami J. Shah
Jami J. Shah The Ohio State University
William N. Rom
William N. Rom New York University
Harvey I. Pass
Harvey I. Pass New York University
Huan Liu
Huan Liu Arizona State University

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