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

147 works between 1990 and 2024
All (147) Journal Article (47) Book Chapter (1) Conference Publication (93) Book (1) Other Outputs (3) Edited Outputs (2)

61 - 80 of 147 works

2012

Journal Article

Introducing cloud computing topics in curricula

Chen, Ling, Liu, Yang, Gallagher, Marcus, Pailthorpe, Bernard, Sadiq, Shazia, Shen, Heng Tao and Li, Xue (2012). Introducing cloud computing topics in curricula. Journal of Information Systems Education, 23 (3), 315-324.

Introducing cloud computing topics in curricula

2012

Conference Publication

Length scale for characterising continuous optimization problems

Morgan, Rachael and Gallagher, Marcus (2012). Length scale for characterising continuous optimization problems. Parallel Problem Solving from Nature - PPSN XII 12th International Conference, Taormina, Italy, 1 - 5 September 2012. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-32937-1_41

Length scale for characterising continuous optimization problems

2012

Conference Publication

Variable screening for reduced dependency modelling in Gaussian-based continuous estimation of distribution algorithms

Mishra, Krishna Manjari and Gallagher, Marcus (2012). Variable screening for reduced dependency modelling in Gaussian-based continuous estimation of distribution algorithms. 2012 IEEE World Congress on Computational Intelligence (IEEE-WCCI 2012), Brisbane, QLD Australia, 10-15 June 2012. Piscataway, NJ United States: IEEE. doi: 10.1109/CEC.2012.6256482

Variable screening for reduced dependency modelling in Gaussian-based continuous estimation of distribution algorithms

2011

Journal Article

Reinforcement learning in first person shooter games

McPartland, Michelle and Gallagher, Marcus (2011). Reinforcement learning in first person shooter games. Ieee Transactions On Computational Intelligence and Ai in Games, 3 (1) 5672586, 43-56. doi: 10.1109/TCIAIG.2010.2100395

Reinforcement learning in first person shooter games

2011

Conference Publication

Faster and parameter-free discord search in quasi-periodic time series

Luo, Wei and Gallagher, Marcus (2011). Faster and parameter-free discord search in quasi-periodic time series. 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Shenzhen, China, 24-27 May 2011. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-20847-8_12

Faster and parameter-free discord search in quasi-periodic time series

2011

Conference Publication

Under voltage load shedding utilizing trajectory sensitivity to enhance voltage stability

Arief, Ardiaty, Nappu, Muhammad Bachtiar, Gallagher, Marcus and Dong, Zhao Yang (2011). Under voltage load shedding utilizing trajectory sensitivity to enhance voltage stability. 21st Australasian Universities Power Engineering Conference (AUPEC) 2011, Brisbane, Australia, 25-28 September 2011. Pitscataway, NJ, United States: IEEE.

Under voltage load shedding utilizing trajectory sensitivity to enhance voltage stability

2010

Journal Article

Using Gaussian process with test rejection to detect T-Cell epitopes in pathogen genomes

You, Liwen, Brusic, Vladimir, Gallagher, Marcus and Boden, Mikael (2010). Using Gaussian process with test rejection to detect T-Cell epitopes in pathogen genomes. IEEE-ACM Transactions on Computational Biology and Bioinformatics, 7 (4) 4695825, 741-751. doi: 10.1109/TCBB.2008.131

Using Gaussian process with test rejection to detect T-Cell epitopes in pathogen genomes

2010

Conference Publication

When does dependency modelling help? Using a randomized landscape generator to compare algorithms in terms of problem structure

Morgan, Rachael and Gallagher, Marcus (2010). When does dependency modelling help? Using a randomized landscape generator to compare algorithms in terms of problem structure. Parallel Problem Solving from Nature, Kraków, Poland, 11-15 September 2010. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-15844-5_10

When does dependency modelling help? Using a randomized landscape generator to compare algorithms in terms of problem structure

2010

Conference Publication

Comparison of CPF and modal analysis methods in determining effective DG locations

Arief, Ardiaty, Nappu, Muhammad Bachtiar, Gallagher, Marcus, Dong, Zhao Yang and Zhao, Junhua (2010). Comparison of CPF and modal analysis methods in determining effective DG locations. 9th International Power and Energy Conference (IPEC), Singapore, 27-29 October 2010. United States: IEEE. doi: 10.1109/IPECON.2010.5697057

Comparison of CPF and modal analysis methods in determining effective DG locations

2010

Conference Publication

Unsupervised DRG upcoding detection in healthcare databases

Luo, Wei and Gallagher, Marcus (2010). Unsupervised DRG upcoding detection in healthcare databases. IEEE International Conference on Data Mining, Sydney, NSW, Australia, 14-17 December 2010. Piscataway, NJ, U.S.A.: IEEE Computer Society. doi: 10.1109/ICDMW.2010.108

Unsupervised DRG upcoding detection in healthcare databases

2010

Conference Publication

Visualising a state-wide patient data collection: A case study to expand the audience for healthcare data

Luo, Wei, Gallagher, Marcus, O'Kane, Di, Connor, Jason, Dooris, Mark, Roberts, Col, Mortimer, Lachlan and Wiles, Janet (2010). Visualising a state-wide patient data collection: A case study to expand the audience for healthcare data. HIKM 2010: 4th Australasian Workshop on Health Informatics and Knowledge Management, Brisbane, Australia, 18-21 January 2010. Sydney, Australia: Australian Computer Society.

Visualising a state-wide patient data collection: A case study to expand the audience for healthcare data

2009

Conference Publication

Convergence analysis of UMDAc with finite populations: A case study on flat landscapes

Yuan, Bo and Gallagher, Marcus (2009). Convergence analysis of UMDAc with finite populations: A case study on flat landscapes. 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009, Montréal, QC, Canada, 8-12 July 2009. New York, NY, U.S.A.: ACM (Association for Computing Machinery) Press. doi: 10.1145/1569901.1569967

Convergence analysis of UMDAc with finite populations: A case study on flat landscapes

2009

Conference Publication

Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noiseless function testbed

Gallagher, Marcus (2009). Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noiseless function testbed. 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference (GECCO'09), Montreal, Canada, 8-12 July 2009. New York, United States: ACM Digital Library. doi: 10.1145/1570256.1570332

Black-box optimization benchmarking: results for the BayEDAcG algorithm on the noiseless function testbed

2009

Conference Publication

An improved small-sample statistical test for comparing the success rates of evolutionary algorithms

Yuan, Bo and Gallagher, Marcus (2009). An improved small-sample statistical test for comparing the success rates of evolutionary algorithms. 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009, Montreal, QC, Canada, 8-12 July 8 2009. New York, NY, United States: ACM. doi: 10.1145/1569901.1570213

An improved small-sample statistical test for comparing the success rates of evolutionary algorithms

2009

Conference Publication

Investigating circles in a square packing problems as a realistic benchmark for continuous metaheuristic optimization algorithms

Marcus Gallagher (2009). Investigating circles in a square packing problems as a realistic benchmark for continuous metaheuristic optimization algorithms. The VIII Metaheuristic International Conference MIC 2009, Hamburg, Germany, 13-16 July, 2009.

Investigating circles in a square packing problems as a realistic benchmark for continuous metaheuristic optimization algorithms

2009

Conference Publication

Black-Box Optimization Benchmarking: Results for the BayEDAcGAlgorithm on the Noiseless Function Testbed

Gallagher, Marcus R. (2009). Black-Box Optimization Benchmarking: Results for the BayEDAcGAlgorithm on the Noiseless Function Testbed. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/1570256.1570318

Black-Box Optimization Benchmarking: Results for the BayEDAcGAlgorithm on the Noiseless Function Testbed

2008

Conference Publication

An influence map model for playing Ms. Pac-Man

Wirth, N. and Gallagher, M. (2008). An influence map model for playing Ms. Pac-Man. IEEE Symposium on Computational Intelligence and Games 2008 (CIG '08), Perth, Australia, 15-18 December 2008. Piscataway, NJ, U.S.A.: IEEE - Institute of Electrical Electronics Engineers Inc.. doi: 10.1109/CIG.2008.5035644

An influence map model for playing Ms. Pac-Man

2008

Conference Publication

Learning to be a Bot: Reinforcement learning in shooter games

McPartland, M. and Gallagher, M. (2008). Learning to be a Bot: Reinforcement learning in shooter games. 4th Artifical Intelligence for Interactive Digital Entertainment Conference, Stanford, California, 22-24 October, 2008. USA: The AAAI Press.

Learning to be a Bot: Reinforcement learning in shooter games

2008

Conference Publication

Creating a multi-purpose first person shooter bot with reinforcement learning

McPartland, M. and Gallagher, M. (2008). Creating a multi-purpose first person shooter bot with reinforcement learning. IEEE Symposium on Computational Intelligence and Games 2008 (CIG '08), Perth, Australia, 15-18 December 2008. Piscataway, NJ, U.S.A.: IEEE. doi: 10.1109/CIG.2008.5035633

Creating a multi-purpose first person shooter bot with reinforcement learning

2008

Conference Publication

Gaussian mixture models in estimations of distribution algotithms: Implementation details and experimental analysis

Kumar, N. and Gallagher, M. (2008). Gaussian mixture models in estimations of distribution algotithms: Implementation details and experimental analysis. 12th Asia-Pacific Symposium on Intelligent and Evolutionary Systems (IES'08), Melbourne, Australia, 7-8 December 2008. Clayton, VIC, Australia: Monash University, Clayton School of Information Technology.

Gaussian mixture models in estimations of distribution algotithms: Implementation details and experimental analysis

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

    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

    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

  • Doctor Philosophy

    Evolving Modular Neural Networks: Investigating the Role of Modularity and Linkage Learning in Neuroevolution

    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

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

    Principal Advisor

    Other advisors: Associate Professor Archie Chapman

  • Doctor Philosophy

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

    Principal Advisor

    Other advisors: Associate Professor Archie Chapman

  • Doctor Philosophy

    Towards Autonomous Network Security

    Associate Advisor

    Other advisors: Dr Siamak Layeghy, Professor Marius Portmann

  • Doctor Philosophy

    Characterizing Influence and Sensitivity in the Interpolating Regime

    Associate Advisor

    Other advisors: Professor Fred Roosta

  • Master Philosophy

    Forecasting and optimising decisions with machine learing

    Associate Advisor

    Other advisors: Dr Slava Vaisman

  • Doctor Philosophy

    Digital simulation and model guided optimisation of light driven cell factories

    Associate Advisor

    Other advisors: Dr Juliane Wolf, Professor Ben Hankamer

  • Doctor Philosophy

    Towards Practical Machine Learning Based Network Intrusion Detection

    Associate Advisor

    Other advisors: Dr Siamak Layeghy, Professor Marius Portmann

  • Doctor Philosophy

    Towards Autonomous Network Security

    Associate Advisor

    Other advisors: Professor Marius Portmann, Dr Siamak Layeghy

  • Doctor Philosophy

    Medical Image Segmentation with Limited Annotated Data

    Associate Advisor

    Other advisors: Professor Brian Lovell

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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