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)

1 - 20 of 147 works

2024

Conference Publication

Analyzing the Runtime of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) on the Concatenated Trap Function

Qiao, Yukai and Gallagher, Marcus (2024). Analyzing the Runtime of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) on the Concatenated Trap Function. New York, NY, USA: ACM. doi: 10.1145/3638530.3664158

Analyzing the Runtime of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) on the Concatenated Trap Function

2024

Conference Publication

Towards an Improved Understanding of Features for More Interpretable Landscape Analysis

Gallagher, Marcus and Munoz, Mario (2024). Towards an Improved Understanding of Features for More Interpretable Landscape Analysis. New York, NY, USA: Association for Computing Machinery, Inc. doi: 10.1145/3638530.3654301

Towards an Improved Understanding of Features for More Interpretable Landscape Analysis

2024

Conference Publication

Searching for Benchmark Problem Instances from Data-Driven Optimisation

Hajari, Sara and Gallagher, Marcus (2024). Searching for Benchmark Problem Instances from Data-Driven Optimisation. New York, NY, USA: ACM. doi: 10.1145/3638530.3654322

Searching for Benchmark Problem Instances from Data-Driven Optimisation

2024

Journal Article

Benchmarking the benchmark — Comparing synthetic and real-world Network IDS datasets

Layeghy, Siamak, Gallagher, Marcus and Portmann, Marius (2024). Benchmarking the benchmark — Comparing synthetic and real-world Network IDS datasets. Journal of Information Security and Applications, 80 103689, 1-18. doi: 10.1016/j.jisa.2023.103689

Benchmarking the benchmark — Comparing synthetic and real-world Network IDS datasets

2024

Journal Article

Feature extraction for machine learning-based intrusion detection in IoT networks

Sarhan, Mohanad, Layeghy, Siamak, Moustafa, Nour, Gallagher, Marcus and Portmann, Marius (2024). Feature extraction for machine learning-based intrusion detection in IoT networks. Digital Communications and Networks, 10 (1), 205-216. doi: 10.1016/j.dcan.2022.08.012

Feature extraction for machine learning-based intrusion detection in IoT networks

2023

Conference Publication

Modularity based linkage model for neuroevolution

Qiao, Yukai and Gallagher, Marcus (2023). Modularity based linkage model for neuroevolution. GECCO '23 Companion: Companion Conference on Genetic and Evolutionary Computation, Lisbon, Portugal, 15-19 July 2023. New York, NY USA: Association for Computing Machinery. doi: 10.1145/3583133.3590648

Modularity based linkage model for neuroevolution

2023

Conference Publication

Towards understanding the link between modularity and performance in neural networks for reinforcement learning

Munn, Humphrey and Gallagher, Marcus (2023). Towards understanding the link between modularity and performance in neural networks for reinforcement learning. International Joint Conference on Neural Networks (IJCNN), Broadbeach, QLD Australia, 18-23 June 2023. New York, NY United States: IEEE Computer Society. doi: 10.1109/ijcnn54540.2023.10191234

Towards understanding the link between modularity and performance in neural networks for reinforcement learning

2023

Journal Article

Guest editorial: special issue on evolutionary computation for games

Schrum, Jacob, Liu, Jialin, Browne, Cameron, Ekárt, Anikó and Gallagher, Marcus (2023). Guest editorial: special issue on evolutionary computation for games. IEEE Transactions on Games, 15 (1), 1-4. doi: 10.1109/tg.2022.3225730

Guest editorial: special issue on evolutionary computation for games

2023

Journal Article

From zero-shot machine learning to zero-day attack detection

Sarhan, Mohanad, Layeghy, Siamak, Gallagher, Marcus and Portmann, Marius (2023). From zero-shot machine learning to zero-day attack detection. International Journal of Information Security, 22 (4), 947-959. doi: 10.1007/s10207-023-00676-0

From zero-shot machine learning to zero-day attack detection

2023

Journal Article

Opioid dispensing 2008–18: a Queensland perspective

Suckling, Benita, Pattullo, Champika, Donovan, Peter, Gallagher, Marcus, Patanwala, Asad and Penm, Jonathan (2023). Opioid dispensing 2008–18: a Queensland perspective. Australian Health Review, 47 (2), 217-225. doi: 10.1071/ah22247

Opioid dispensing 2008–18: a Queensland perspective

2022

Conference Publication

Examining average and discounted reward optimality criteria in reinforcement learning

Dewanto, Vektor and Gallagher, Marcus (2022). Examining average and discounted reward optimality criteria in reinforcement learning. 35th Australasian Joint Conference on Artificial Intelligence (AI), Perth, Australia, 5-9 December 2022. Heidelberg, Germany: Springer. doi: 10.1007/978-3-031-22695-3_56

Examining average and discounted reward optimality criteria in reinforcement learning

2022

Journal Article

An agile new research framework for hybrid human-AI teaming: trust, transparency, and transferability

Caldwell, Sabrina, Sweetser, Penny, O'donnell, Nicholas, Knight, Matthew J., Aitchison, Matthew, Gedeon, Tom, Johnson, Daniel, Brereton, Margot, Gallagher, Marcus and Conroy, David (2022). An agile new research framework for hybrid human-AI teaming: trust, transparency, and transferability. ACM Transactions on Interactive Intelligent Systems, 12 (3) 17, 1-36. doi: 10.1145/3514257

An agile new research framework for hybrid human-AI teaming: trust, transparency, and transferability

2022

Conference Publication

Pittsburgh learning classifier systems for explainable reinforcement learning: comparing with XCS

Bishop, Jordan T., Gallagher, Marcus and Browne, Will N. (2022). Pittsburgh learning classifier systems for explainable reinforcement learning: comparing with XCS. Genetic and Evolutionary Computation Conference (GECCO), Boston, MA, United States, 9-13 July 2022. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3512290.3528767

Pittsburgh learning classifier systems for explainable reinforcement learning: comparing with XCS

2022

Conference Publication

Graph neural network-based android malware classification with jumping knowledge

Lo, Wai Weng, Layeghy, Siamak, Sarhan, Mohanad, Gallagher, Marcus and Portmann, Marius (2022). Graph neural network-based android malware classification with jumping knowledge. 2022 IEEE Conference on Dependable and Secure Computing (DSC), Edinburgh, United Kingdom, 22-24 June 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/dsc54232.2022.9888878

Graph neural network-based android malware classification with jumping knowledge

2022

Conference Publication

E-GraphSAGE: a graph neural network based intrusion detection system for IoT

Lo, Wai Weng, Layeghy, Siamak, Sarhan, Mohanad, Gallagher, Marcus and Portmann, Marius (2022). E-GraphSAGE: a graph neural network based intrusion detection system for IoT. NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 25-29 April 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/noms54207.2022.9789878

E-GraphSAGE: a graph neural network based intrusion detection system for IoT

2021

Journal Article

Using regression models for characterizing and comparing black box optimization problems

Saleem, Sobia and Gallagher, Marcus (2021). Using regression models for characterizing and comparing black box optimization problems. Swarm and Evolutionary Computation, 68 100981, 1-10. doi: 10.1016/j.swevo.2021.100981

Using regression models for characterizing and comparing black box optimization problems

2021

Conference Publication

A genetic fuzzy system for interpretable and parsimonious reinforcement learning policies

Bishop, Jordan T., Gallagher, Marcus and Browne, Will N. (2021). A genetic fuzzy system for interpretable and parsimonious reinforcement learning policies. GECCO '21: Genetic and Evolutionary Computation Conference, Lille, France, 10 - 14 July, 2021. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3449726.3463198

A genetic fuzzy system for interpretable and parsimonious reinforcement learning policies

2021

Conference Publication

Avoiding kernel fixed points: Computing with ELU and GELU infinite networks

Tsuchida, Russell, Pearce, Tim, van der Heide, Chris, Roosta, Fred and Gallagher, Marcus (2021). Avoiding kernel fixed points: Computing with ELU and GELU infinite networks. 35th AAAI Conference on Artificial Intelligence, AAAI 2021, Online, 2 - 9 February 2021. Menlo Park, CA United States: Association for the Advancement of Artificial Intelligence.

Avoiding kernel fixed points: Computing with ELU and GELU infinite networks

2021

Conference Publication

Avoiding kernel fixed points: computing with ELU and GELU infinite networks

Tsuchida, Russell, Pearce, Tim, van der Heide, Chris, Roosta, Fred and Gallagher, Marcus (2021). Avoiding kernel fixed points: computing with ELU and GELU infinite networks. 35th AAAI Conference on Artificial Intelligence / 33rd Conference on Innovative Applications of Artificial Intelligence / 11th Symposium on Educational Advances in Artificial Intelligence, Electr Network, 2-9 February 2021. Washington, DC, United States: Association for the Advancement of Artificial Intelligence.

Avoiding kernel fixed points: computing with ELU and GELU infinite networks

2020

Conference Publication

Optimality-based analysis of xcsf compaction in discrete reinforcement learning

Bishop, Jordan T. and Gallagher, Marcus (2020). Optimality-based analysis of xcsf compaction in discrete reinforcement learning. 16th International Conference on Parallel Problem Solving from Nature PPSN 2020, Leiden, Netherlands, September 5-9, 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-58115-2_33

Optimality-based analysis of xcsf compaction in discrete reinforcement learning

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

    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

    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

    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

    Towards Autonomous Network Security

    Associate Advisor

    Other advisors: Dr Siamak Layeghy, Associate 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

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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