World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
44
Citations
8038
World Ranking
1587
National Ranking
684

Overview

George C. Runger is affiliated with Arizona State University in the United States. Their research primarily focuses on various domains within computer science, with a particular emphasis on artificial intelligence and its applications.

The scientist's main fields of study include:

  • Computer Science

Within this broad area, their subfields of study cover:

  • Artificial Intelligence
  • General Health Professions
  • Industrial and Manufacturing Engineering
  • Food Science
  • Molecular Biology

Their work addresses multiple topics, notably:

  • Machine Learning and Algorithms
  • Scheduling and Optimization Algorithms
  • Gaussian Processes and Bayesian Inference
  • Food Supply Chain Traceability
  • Food Waste Reduction and Sustainability
  • Health disparities and outcomes
  • Food Security and Health in Diverse Populations

George C. Runger has published research in various scientific venues, including:

  • Test
  • arXiv (Cornell University)
  • BMC Bioinformatics
  • SSRN Electronic Journal
  • Journal of Cleaner Production

Recent papers authored by or involving George C. Runger include:

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

The scientist frequently collaborates with a group of co-authors that includes:

  • Li Liu
  • Kailey Love
  • Seho Kee
  • Mani Janakiram
  • Enrique Del Castillo

Best Publications

  • Comparisons of Multivariate CUSUM Charts

    Joseph J. Pignatiello;George C. Runger

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

    Sharad S. Prabhu;Douglas C. Montgomery;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

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

    Douglas C. Montgomery;George C. Runger

  • Adaptive Sampling for Process Control

    George C. Runger;Joseph J. Pignatiello

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

    George C. Runger;Sharad S. Prabhu

  • X¯ chart with adaptive sample sizes

    Unknown

  • Applied Statistics and Probability for Engineers sixth edition

    Douglas C. Montgomery;George C. Runger

  • Optimization Problems and Methods in Quality Control and Improvement

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

  • The Analysis of Transformed Data

    Unknown

  • 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

  • Contributors to a multivariate statistical process control chart signal

    George C. Runger;Frank B. Alt;Douglas C. Montgomery

  • Model-Based and Model-Free Control of Autocorrelated Processes

    Unknown

  • Guidelines for the application of adaptive control charting schemes

    Lora S. Zimmer;Douglas C. Montgomery;George C. Runger

  • Multivariate statistical process monitoring and diagnosis with grouped regression‐adjusted variables

    Daryl J. Hauck;George C. Runger;Douglas C. Montgomery

  • Designing control charts using an empirical reference distribution

    Unknown

  • Foldovers of 2k-p Resolution IV Experimental Designs

    Douglas C. Montgomery;George C. Runger

  • A review of statistical process control techniques for short run manufacturing systems

    Enrique Del Castillo;James M. Grayson;Douglas C. Montgomery;George C. Runger

  • Optimal monitoring of multivariate data for fault patterns

    George C. Runger;Russell Richard Barton;Enrique Del Castillo;William H. Woodall

  • See the forest before the trees: fine-tuned learning and its application to the traveling salesman problem

    S.P. Coy;B.L. Golden;G.C. Runger;E.A. Wasil

  • Applied Statistics and Probability for Engineers, Student Solutions Manual

    Douglas C. Montgomery;George C. Runger

  • Adaptive controllers to integrate SPC and EPC

    Yuehjen E. Shao;George C. Runger;Jorge Haddock;W. A. Wallace

  • Neural network models for initial public offerings

    Steven J. Robertson;Bruce L. Golden;George C. Runger;Edward A. Wasil

  • Statistical process control using level crossings

    Thomas R. Willemain;George C. Runger

  • Statistical process control using run sums

    Thomas R. Willemain;George C. Runger

  • On active learning methods for manifold data

    Hang Li;Enrique Del Castillo;George Runger

  • Optimal multivariate bounded adjustment

    George Runger;Zilong Lian;Enrique Del Castillo

  • Probability and Statistics for Engineers

    George C. Runger;Douglas C. Montgomery

  • An introduction to a new journal for Healthcare Systems Engineering

    John W. Fowler;James C. Benneyan;Pascale Carayon;Brian T. Denton

  • Most powerful invariant permutation tests

    George C. Runger;M. L. Eaton

Frequent Co-Authors

Douglas C. Montgomery
Douglas C. Montgomery Arizona State University

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