![Associate Professor Marcus Gallagher](/sites/default/files/profiles/469.jpeg)
Overview
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
Qualifications
- Bachelor of Computer Science, University of New England Australia
- Postgraduate Diploma, University of New England Australia
- Doctor of Philosophy, The University of Queensland
Works
Search Professor Marcus Gallagher’s works on UQ eSpace
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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.
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
2020
Journal Article
Considerations for selecting a machine learning technique for predicting deforestation
Mayfield, Helen J. , Smith, Carl , Gallagher, Marcus and Hockings, Marc (2020). Considerations for selecting a machine learning technique for predicting deforestation. Environmental Modelling and Software, 131 104741, 1-10. doi: 10.1016/j.envsoft.2020.104741
Funding
Supervision
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
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2024
Doctor Philosophy
Fitness Landscape Features as Curriculum Ordering Measures for Reinforcement Learning
Principal Advisor
-
2023
Doctor Philosophy
Parsimony and Performance in Rule-Based Evolutionary Reinforcement Learning
Principal Advisor
-
2022
Doctor Philosophy
Discounting-free Policy Gradient Reinforcement Learning from Transient States
Principal Advisor
Other advisors: Professor Fred Roosta
-
2021
Master Philosophy
Stochaskell: A common platform for probabilistic programming research and applications
Principal Advisor
Other advisors: Dr Thomas Taimre
-
2021
Doctor Philosophy
Improved Evaluation of Existing Methods in Landscape Analysis and Comparison of Black Box Optimization Problems using Regression Models
Principal Advisor
Other advisors: Dr Ian Wood
-
2020
Doctor Philosophy
Results on Infinitely Wide Multi-layer Perceptrons
Principal Advisor
Other advisors: Professor Fred Roosta
-
2015
Doctor Philosophy
Analysing and Comparing Problem Landscapes for Black-Box Optimization via Length Scale
Principal Advisor
-
2015
Doctor Philosophy
Data-Driven Analysis of Variables and Dependencies in Continuous Optimization Problems and Estimation of Distribution Algorithms.
Principal Advisor
Other advisors: Dr Ian Wood
-
2014
Doctor Philosophy
Towards a Biologically Plausible Computational Model of Developmental Learning with Robotic Applications
Principal Advisor
-
2013
Doctor Philosophy
Training Bots to Play: Investigating Interactive Reinforcement Learning for Bot Behaviours in Shooter Games
Principal Advisor
-
2013
Doctor Philosophy
Advanced Computational Methods for System Voltage Stability Enhancement
Principal Advisor
-
2010
Master Philosophy
GMMEDA : A demonstration of probabilistic modelling in continuous metaheuristic optimization using mixture models
Principal Advisor
-
2010
Doctor Philosophy
Optimal active learning: experimental factors and membership query learning
Principal Advisor
Other advisors: Professor Janet Wiles
-
2009
Doctor Philosophy
The Development and Application of Statistical and Machine Learning Techniques in Probabilistic Astronomical Catalogue-Matching Problems
Principal Advisor
-
2009
Doctor Philosophy
Kinematic and Elasto-Dynamic Design Optimisation of a Class of Parallel Kinematic Machines
Principal Advisor
-
2006
Doctor Philosophy
TOWARDS IMPROVED EXPERIMENTAL EVALUATION AND COMPARISON OF EVOLUTIONARY ALGORITHMS
Principal Advisor
-
Doctor Philosophy
TOPOLOGICAL MODELS OF TRANSMEMBRANE PROTEINS FOR SUBCELLULAR LOCALIZATION PREDICTION
Principal Advisor
Other advisors: Professor Mikael Boden, Professor Geoffrey McLachlan
-
2024
Doctor Philosophy
Investigating the use of Computer Vision Techniques for Analysing the Surf Zone and Swash Zone
Associate Advisor
Other advisors: Professor Tom Baldock
-
2024
Doctor Philosophy
Approaches to scalable, sustainable, and ethical natural language processing research in the face of rapid development
Associate Advisor
Other advisors: Professor Janet Wiles
-
2023
Doctor Philosophy
The Detection of Network Cyber Attacks Using Machine Learning
Associate Advisor
Other advisors: Dr Siamak Layeghy, Associate Professor Marius Portmann
-
2023
Master Philosophy
Graph Representation Learning for Cyberattack Detection and Forensics
Associate Advisor
Other advisors: Dr Siamak Layeghy, Associate Professor Marius Portmann
-
2022
Doctor Philosophy
Efficient second-order optimisation methods for large scale machine learning
Associate Advisor
Other advisors: Professor Fred Roosta
-
2018
Doctor Philosophy
Smart Deployment of Community Energy Storage in Power Grid with PV Units
Associate Advisor
Other advisors: Professor Mithulan Nadarajah
-
2015
Master Philosophy
Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging
Associate Advisor
-
2015
Doctor Philosophy
Biometric Markers for Affective Disorders
Associate Advisor
Other advisors: Professor Mikael Boden
-
2015
Master Philosophy
Large Scale Material Science Data Analysis
Associate Advisor
Other advisors: Professor Helen Huang
-
2015
Doctor Philosophy
Multi-step forecasts of complex dynamical systems using soft-computing tools, with application to crude oil returns
Associate Advisor
-
2015
Doctor Philosophy
Making the most of machine learning and freely available datasets: A deforestation case study
Associate Advisor
Other advisors: Emeritus Professor Marc Hockings
-
2014
Doctor Philosophy
Machine Learning as an Adjunct to Clinical Decision Making in Alcohol Dependence Treatment
Associate Advisor
Other advisors: Professor Jason Connor
-
2014
Doctor Philosophy
Group-based Classification with an Application in Cervical Cancer Screening
Associate Advisor
-
2014
Doctor Philosophy
Estimation of Distribution Algorithms for Single- and Multi-Objective Optimization
Associate Advisor
Other advisors: Dr Ian Wood, Professor Dirk Kroese
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2008
Doctor Philosophy
Visual Learning for Mobile Robot Localisation
Associate Advisor
-
2008
Doctor Philosophy
Adaptation by prediction: Reading the play in robot soccer
Associate Advisor
-
2006
Doctor Philosophy
Implementing blind source separation in signal processing and telecommunications
Associate Advisor
-
2005
Doctor Philosophy
THE NATURE OF CHANGE IN COMPLEX, SOCIO-TECHNICAL SYSTEMS
Associate Advisor
-
2005
Doctor Philosophy
Application of the Tree Augmented Naive Bayes Network to Classification and Forecasting
Associate Advisor
-
2004
Doctor Philosophy
FAST LEARNING IN BOLTZMANN MACHINES
Associate Advisor
Media
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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