Associate Professor Marcus Gallagher
Associate Professor

Marcus Gallagher

Email: 
Phone: 
+61 7 336 56197

Background

Marcus Gallagher is an Associate Professor in the Artificial Intelligence Group in the School of Information Technology and Electrical Engineering. His research interests are in artificial intelligence, including optimisation and machine learning. He is particularly interested in understanding the relationship between algorithm performance and problem structure via benchmarking. My work includes cross-disciplinary collaborations and real-world applications of AI techniques.

Dr Gallagher received his BCompSc and GradDipSc from the University of New England, Australia in 1994 and 1995 respectively, and his PhD in 2000 from the University of Queensland, Australia. He also completed a GradCert (Higher Education) in 2010.

Availability

Associate Professor Marcus Gallagher is:
Available for supervision
Media expert

Fields of research

Biomedical and Clinical Sciences Health Sciences Information and Computing Sciences Mathematical Sciences Numerical and computational mathematics Optimisation Statistics Theory of computation

Qualifications

  • Bachelor of Computer Science, University of New England Australia
  • Postgraduate Diploma, University of New England Australia
  • Doctor of Philosophy, The University of Queensland

Search Professor Marcus Gallagher’s works on UQ eSpace

146 works between 1990 and 2024
All (146) Journal Article (47) Book Chapter (1) Conference Publication (92) Book (1) Other Outputs (3) Edited Outputs (2)

101 - 120 of 146 works

2005

Conference Publication

On the importance of diversity maintenance in estimation of distribution algorithms

Yuan, B. and Gallagher, M. R. (2005). On the importance of diversity maintenance in estimation of distribution algorithms. 7th Annual Genetic and Evolutionary Computation Conference GECCO 2005, Washington DC, USA, 25-29 June, 2005. New York, USA: ACM Press. doi: 10.1145/1068009.1068129

On the importance of diversity maintenance in estimation of distribution algorithms

2005

Other Outputs

McCulloch-Pitts Network

Gallagher, M. R. (2005). McCulloch-Pitts Network.

McCulloch-Pitts Network

2005

Edited Outputs

Intelligent Data Engineering and Automated Learning - IDEAL2005

Marcus Gallagher, James Hogan and Frederic Maire eds. (2005). Intelligent Data Engineering and Automated Learning - IDEAL2005. 6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005: Lecture Notes in Computer Science (journal), Brisbane, Australia, 6-8 July 2005. Germany: Springer.

Intelligent Data Engineering and Automated Learning - IDEAL2005

2005

Conference Publication

An empirical study of Hoelfding Racing for model selction in K-nearest neighbor classification

Yeh, Y. and Gallagher, M. R. (2005). An empirical study of Hoelfding Racing for model selction in K-nearest neighbor classification. Intelligent Data Engineering and Automated Learning - IDEAL205, Brisbane, Australia, 6-8 July, 2005. Berlin, Germany: Springer. doi: 10.1007/11508069_29

An empirical study of Hoelfding Racing for model selction in K-nearest neighbor classification

2005

Conference Publication

A hybrid approach to parameter tuning in genetic algorithms

Yuan, B. and Gallagher, M. R. (2005). A hybrid approach to parameter tuning in genetic algorithms. 2005 IEEE Congress on Evolutionary Computation (IEEE CEC 2005), Edinburgh, Scotland, 2-5 September 2005. U.S.A.: IEEE.

A hybrid approach to parameter tuning in genetic algorithms

2005

Conference Publication

MRI magnet design: Search space analysis, EDAs and a real-world problem with significant dependencies

Yuan, B., Gallagher, M. R. and Crozier, S. (2005). MRI magnet design: Search space analysis, EDAs and a real-world problem with significant dependencies. 7th Annual Genetic and Evolutionary Computation Conference - GELCCO 2005, Washington DC, USA, 25-29 June, 2005. New York, USA: ACM Press. doi: 10.1145/1068009.1068362

MRI magnet design: Search space analysis, EDAs and a real-world problem with significant dependencies

2005

Journal Article

Population-based continuous optimization, probabilistic modelling and mean shift

Gallagher, M. and Frean, M. (2005). Population-based continuous optimization, probabilistic modelling and mean shift. Evolutionary Computation, 13 (1), 29-42. doi: 10.1162/1063656053583478

Population-based continuous optimization, probabilistic modelling and mean shift

2005

Other Outputs

Perceptron

Gallagher, M. R. (2005). Perceptron.

Perceptron

2004

Journal Article

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

Yuan, Bo and Gallagher, Marcus (2004). Statistical racing techniques for improved empirical evaluation of evolutionary algorithms. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3242, 172-181.

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

2004

Journal Article

Machine learning for matching astronomy catalogues

Rohde, David, Drinkwater, Michael, Gallagher, Marcus, Downs, Tom and Doyle, Marianne (2004). Machine learning for matching astronomy catalogues. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3177, 702-707.

Machine learning for matching astronomy catalogues

2004

Conference Publication

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

Yuan, B. and Gallagher, M. R. (2004). Statistical racing techniques for improved empirical evaluation of evolutionary algorithms. The Eighth International Conference on Parallel Problem Solving from Nature, Birmingham, U.K., 18-22 September 2004. Berlin: Springer-Verlag.

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

2004

Conference Publication

Machine learning for matching astronomy catalogues

Rohde, D. J., Drinkwater, M. J., Gallagher, M. R., Downs, T. and Doyle, M. T. (2004). Machine learning for matching astronomy catalogues. The Fifth International Intelligent Data Engineering and Automated Learning Conference (IDEAL 2004), Exeter, U.K., 25-27 August 2004. Berlin, Germany: Springer.

Machine learning for matching astronomy catalogues

2003

Journal Article

Visualization of learning in multilayer perceptron networks using principal component analysis

Gallagher, M. R. and Downs, T. (2003). Visualization of learning in multilayer perceptron networks using principal component analysis. IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Part B-cybernetics, 33 (1), 28-34. doi: 10.1109/TSMCB.2003.808183

Visualization of learning in multilayer perceptron networks using principal component analysis

2003

Conference Publication

On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator

Yuan, B. and Gallagher, M. R. (2003). On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator. The 2003 Congress on Evolutionary Computation (CEC '03), Canberra, Australia, 8-12 December 2003. Piscataway, NJ, U.S.A.: The Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2003.1299610

On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator

2003

Conference Publication

Learning to play Pac-Man: An evolutionary, rule-based approach

Gallagher, M. R. and Ryan, A. J. (2003). Learning to play Pac-Man: An evolutionary, rule-based approach. The 2003 Congress on Evolutionary Computation (CEC 2003), Canberra, Australia, 8-12 December 2003. Piscataway, NJ, U.S.A.: The Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2003.1299397

Learning to play Pac-Man: An evolutionary, rule-based approach

2003

Conference Publication

Playing in continuous spaces: Some analysis and extension of population-based incremental learning

Yuan, B. and Gallagher, M. R. (2003). Playing in continuous spaces: Some analysis and extension of population-based incremental learning. 2003 Congress on Evolutionary Computation (CEC '03), Canberra, Australia, 8-12 December 2003. Piscataway, NJ, U.S.A.: The Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2003.1299609

Playing in continuous spaces: Some analysis and extension of population-based incremental learning

2003

Conference Publication

Blind separation of noisy mixtures using the SAND algorithm

Leong, W. Y., Homer, J. P. and Gallagher, M. R. (2003). Blind separation of noisy mixtures using the SAND algorithm. The Seventh International Symposium on DSP for Communication System and the Second Workshop on the Internet, Telecommunication and Signal Processing, Coolangatta, 8-11 Decmber, 2003. Wollongong: The University of Wollongong.

Blind separation of noisy mixtures using the SAND algorithm

2002

Journal Article

Empirical evidence for ultrametric structure in multi-layer perceptron error surfaces

Gallagher, Marcus, Downs, Tom and Wood, Ian (2002). Empirical evidence for ultrametric structure in multi-layer perceptron error surfaces. Neural Processing Letters, 16 (2), 177-186. doi: 10.1023/A:1019956303894

Empirical evidence for ultrametric structure in multi-layer perceptron error surfaces

2002

Conference Publication

Neural networks and the classification of mineralogical samples using x-ray spectra

Gallagher, M. R. and Deacon, P. (2002). Neural networks and the classification of mineralogical samples using x-ray spectra. Ninth International Conference on Neural Information Processing, Singapore, 18-22 November, 2002. Piscataway, NJ: The Institute of Electrical and Electronics Engineers. doi: 10.1109/ICONIP.2002.1201983

Neural networks and the classification of mineralogical samples using x-ray spectra

2001

Conference Publication

Fitness distance correlation of neural network error surfaces: A scalable, continuous optimization problem

Gallagher, M. R. (2001). Fitness distance correlation of neural network error surfaces: A scalable, continuous optimization problem. Twelfth European Conference on Machine Learning, Freiburg, Germany, 3-7 September, 2001. Berlin: Springer-Verlag.

Fitness distance correlation of neural network error surfaces: A scalable, continuous optimization problem

Past funding

  • 2021 - 2022
    Solving Realistic Portfolio Optimisation Problems Using Interactive Multiobjective Evolutionary Algorithms (Defence Science and Technology Group grant administered by The University of Melbourne)
    University of Melbourne
    Open grant
  • 2019
    Machine Learning for Automated Network Anomaly Detection, Cyber Security and Analysis - Phase II
    Innovation Connections
    Open grant
  • 2018 - 2019
    Machine Learning for Automated Network Anomaly detection and Analysis
    Innovation Connections
    Open grant
  • 2016 - 2020
    Active and interactive analysis of prescription data for harm minimisation
    ARC Linkage Projects
    Open grant
  • 2013 - 2016
    The Development of Automated Advanced Data Analysis Techniques for the Detection of Aberrant Patterns of Prescribing Controlled Drugs
    ARC Linkage Projects
    Open grant
  • 2011 - 2013
    Data Mining Applications in the Regulation of Prescription Opioids
    Queensland Health
    Open grant
  • 2010 - 2012
    Understanding Patient Flow Bottlenecks and Patterns from Hospital Information Systems Data
    UQ Collaboration and Industry Engagement Fund
    Open grant
  • 2007 - 2009
    Metaheuristic Algorithms for Realistic Optimization Problems
    UQ Early Career Researcher
    Open grant
  • 2005 - 2006
    The Application of Machine Learning Techniques in Predicting Medical Outcomes
    UQ FirstLink Scheme
    Open grant
  • 2005 - 2006
    Smart Astronomy: Using Computational Science To Understand Distant Radio Galaxies
    ARC Special Research Initiatives - E-Research
    Open grant
  • 2005 - 2007
    A New Parallel Robot with breakthrough performance for Manufacturing of Aerospace Components - kinematic and dynamic synthesis, design optimisation and prototyping
    ARC Linkage Projects
    Open grant
  • 2001
    Population-based optimization algorithms and probabilistic modelling
    UQ New Staff Research Start-Up Fund
    Open grant

Availability

Associate Professor Marcus Gallagher is:
Available for supervision

Before you email them, read our advice on how to contact a supervisor.

Supervision history

Current supervision

  • Doctor Philosophy

    Multi-objective optimisation and multi-agent learning for IoT devices.

    Principal Advisor

    Other advisors: Associate Professor Archie Chapman

  • Doctor Philosophy

    Hybrid local/global optimisation for the design of diverse structures

    Principal Advisor

  • Doctor Philosophy

    Improving neuroevolution using ideas from deep learning and optimization

    Principal Advisor

    Other advisors: Associate Professor Archie Chapman

  • Doctor Philosophy

    Generating data-driven continuous optimization problems for benchmarking

    Principal Advisor

    Other advisors: Professor Brian Lovell

  • Doctor Philosophy

    Adaptive Curriculums for Robotic Reinforcement Learning

    Principal Advisor

  • Master Philosophy

    Forecasting and optimising decisions with machine learing

    Associate Advisor

  • Doctor Philosophy

    Towards Autonomous Network Security

    Associate Advisor

    Other advisors: Associate Professor Marius Portmann, Dr Siamak Layeghy

  • Doctor Philosophy

    Medical Image Segmentation with Limited Annotated Data

    Associate Advisor

    Other advisors: Professor Brian Lovell

  • Doctor Philosophy

    Characterizing Influence and Sensitivity in the Interpolating Regime

    Associate Advisor

    Other advisors: Professor Fred Roosta

Completed supervision

Enquiries

Contact Associate Professor Marcus Gallagher directly for media enquiries about:

  • Artificial Intelligence
  • Big Data
  • Computer programming
  • Data Science
  • Evolutionary algorithms
  • Evolutionary Computation
  • Heuristic optimisation
  • High-dimensional data - visualisation in computers
  • Intelligent systems
  • Machine learning
  • Neural networks
  • Optimisation Algorithms
  • Search space analysis - IT

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