![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
2020
Journal Article
Predicting alcohol dependence treatment outcomes: A prospective comparative study of clinical psychologists vs ‘trained’ machine learning models
Symons, Martyn, Feeney, Gerald F. X., Gallagher, Marcus R., Young, Ross Mc D. and Connor, Jason P. (2020). Predicting alcohol dependence treatment outcomes: A prospective comparative study of clinical psychologists vs ‘trained’ machine learning models. Addiction, 115 (11) add.15038, 2164-2175. doi: 10.1111/add.15038
2020
Conference Publication
A novel mutation operator for variable length algorithms
Van Ryt, Saskia, Gallagher, Marcus and Wood, Ian (2020). A novel mutation operator for variable length algorithms. AI 2020: Advances in Artificial Intelligence: 33rd Australasian Joint Conference, Canberra, ACT, Australia, 29 - 30 November 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-64984-5_14
2020
Conference Publication
An Implementation and Experimental Evaluation of a Modularity Explicit Encoding Method for Neuroevolution on Complex Learning Tasks
Qiao, Yukai and Gallagher, Marcus (2020). An Implementation and Experimental Evaluation of a Modularity Explicit Encoding Method for Neuroevolution on Complex Learning Tasks. 33rd Australasian Joint Conference, AI 2020, Canberra, ACT Australia, 29–30 November 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-64984-5_11
2020
Book
AI 2020: advances in artificial intelligence
Marcus Gallagher, Nour Moustafa and Erandi Lakshika eds. (2020). AI 2020: advances in artificial intelligence. Lecture Notes in Computer Science, Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-64984-5
2020
Conference Publication
Fitness landscape features and reward shaping in reinforcement learning policy spaces
du Preez-Wilkinson, Nathaniel and Gallagher, Marcus (2020). Fitness landscape features and reward shaping in reinforcement learning policy spaces. Parallel Problem Solving from Nature – PPSN XVI, Leiden, The Netherlands, 5 - 9 September 2020. Heidelberg, Germany: Springer. doi: 10.1007/978-3-030-58115-2_35
2019
Journal Article
Network analysis and visualisation of opioid prescribing data
Hu, Xuelei, Gallagher, Marcus, Loveday, William, Dev, Abhilash and Connor, Jason P. (2019). Network analysis and visualisation of opioid prescribing data. IEEE Journal of Biomedical and Health Informatics, 24 (5) 8822723, 1-9. doi: 10.1109/jbhi.2019.2939028
2019
Conference Publication
Reversible jump probabilistic programming
Roberts, David A., Gallagher, Marcus and Taimre, Thomas (2019). Reversible jump probabilistic programming. The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019), Naha, Okinawa, Japan, 16 - 18 April 2019. Brookline, MA, United States: ML Research Press.
2019
Journal Article
Machine learning vs addiction therapists: a pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medication
Symons, Martyn, Feeney, Gerald F.X., Gallagher, Marcus R., Young, Ross McD. and Connor, Jason P. (2019). Machine learning vs addiction therapists: a pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medication. Journal of Substance Abuse Treatment, 99, 156-162. doi: 10.1016/j.jsat.2019.01.020
2019
Journal Article
Quantitative measure of nonconvexity for black-box continuous functions
Tamura, Kenichi and Gallagher, Marcus (2019). Quantitative measure of nonconvexity for black-box continuous functions. Information Sciences, 476, 64-82. doi: 10.1016/j.ins.2018.10.009
2019
Conference Publication
Fitness landscape analysis in data-driven optimization: An investigation of clustering problems
Gallagher, Marcus (2019). Fitness landscape analysis in data-driven optimization: An investigation of clustering problems. IEEE Congress on Evolutionary Computation (IEEE CEC), Wellington, New Zealand, 10-13 June, 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2019.8790323
2019
Conference Publication
Exchangeability and kernel invariance in trained MLPs
Tsuchida, Russell, Roosta, Fred and Gallagher, Marcus (2019). Exchangeability and kernel invariance in trained MLPs. Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19, Macao, China, 10-16 August 2019. Marina del Rey, CA USA: International Joint Conferences on Artificial Intelligence. doi: 10.24963/ijcai.2019/498
2019
Conference Publication
Exploring the MLDA benchmark on the Nevergrad platform
Rapin, Jeremy, Gallagher, Marcus, Kerschke, Pascal, Preuss, Mike and Teytaud, Olivier (2019). Exploring the MLDA benchmark on the Nevergrad platform. 2019 Genetic and Evolutionary Computation Conference, GECCO 2019, Prague, Czech Republic, 13 - 17 July 2019. New York, New York, USA: Association for Computing Machinery, Inc. doi: 10.1145/3319619.3326830
2018
Journal Article
Direct feature evaluation in black-box optimization using problem transformations
Saleem, Sobia, Gallagher, Marcus and Wood, Ian (2018). Direct feature evaluation in black-box optimization using problem transformations. Evolutionary Computation, 27 (1), 75-98. doi: 10.1162/evco_a_00247
2018
Conference Publication
A model-based framework for black-box problem comparison using gaussian processes
Saleem, Sobia, Gallagher, Marcus and Wood, Ian (2018). A model-based framework for black-box problem comparison using gaussian processes. 15th International Conference on Parallel Problem Solving from Nature, PPSN 2018, Coimbra, Portugal, 8-12 September 2018. Cham, Switzerland: Springer Verlag. doi: 10.1007/978-3-319-99259-4_23
2018
Conference Publication
Flood-fill Q-learning updates for learning redundant policies in order to interact with a computer screen by clicking
du Preez-Wilkinson, Nathaniel, Gallagher, Marcus and Hu, Xuelei (2018). Flood-fill Q-learning updates for learning redundant policies in order to interact with a computer screen by clicking. 31st Australasian Joint Conference on Artificial Intelligence, AI 2018, Wellington,, December 11, 2018-December 14, 2018. Germany: Springer Verlag. doi: 10.1007/978-3-030-03991-2_49
2018
Conference Publication
Invariance of weight distributions in rectified MLPs
Tsuchida, Russell, Roosta-Khorasani, Farbod and Gallagher, Marcus (2018). Invariance of weight distributions in rectified MLPs. 35th International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018. Cambridge, MA, United States: M I T Press.
2018
Conference Publication
Intra-task curriculum learning for faster reinforcement learning in video games
du Preez-Wilkinson, Nathaniel, Gallagher, Marcus and Hu, Xuelei (2018). Intra-task curriculum learning for faster reinforcement learning in video games. 31st Australasian Joint Conference on Artificial Intelligence (AI 2018), Wellington, New Zealand, 11-14 December 2018. Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_6
2017
Journal Article
Parallel evolutionary algorithm for single and multi-objective optimisation: Differential evolution and constraints handling
Pedroso, Dorival M., Bonyadi, Mohammad Reza and Gallagher, Marcus (2017). Parallel evolutionary algorithm for single and multi-objective optimisation: Differential evolution and constraints handling. Applied Soft Computing, 61, 995-1012. doi: 10.1016/j.asoc.2017.09.006
2017
Journal Article
Multiple community energy storage planning in distribution networks using a cost-benefit analysis
Sardi, Junainah, Mithulananthan, N., Gallagher, M. and Hung, Duong Quoc (2017). Multiple community energy storage planning in distribution networks using a cost-benefit analysis. Applied Energy, 190, 453-463. doi: 10.1016/j.apenergy.2016.12.144
2017
Journal Article
Use of freely available datasets and machine learning methods in predicting deforestation
Mayfield, Helen, Smith, Carl, Gallagher, Marcus and Hockings, Marc (2017). Use of freely available datasets and machine learning methods in predicting deforestation. Environmental Modelling and Software, 87, 17-28. doi: 10.1016/j.envsoft.2016.10.006
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
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
Generating data-driven continuous optimization problems for benchmarking
Principal Advisor
Other advisors: Professor Brian Lovell
-
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
-
Master Philosophy
Forecasting and optimising decisions with machine learing
Associate Advisor
Completed supervision
-
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
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
-
2021
Master Philosophy
Stochaskell: A common platform for probabilistic programming research and applications
Principal Advisor
Other advisors: Dr Thomas Taimre
-
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
Master Philosophy
Large Scale Material Science Data Analysis
Associate Advisor
Other advisors: Professor Helen Huang
-
2015
Doctor Philosophy
Biometric Markers for Affective Disorders
Associate Advisor
Other advisors: Professor Mikael Boden
-
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
Estimation of Distribution Algorithms for Single- and Multi-Objective Optimization
Associate Advisor
Other advisors: Dr Ian Wood, Professor Dirk Kroese
-
2014
Doctor Philosophy
Group-based Classification with an Application in Cervical Cancer Screening
Associate Advisor
-
-
2008
Doctor Philosophy
Adaptation by prediction: Reading the play in robot soccer
Associate Advisor
-
2008
Doctor Philosophy
Visual Learning for Mobile Robot Localisation
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
Need help?
For help with finding experts, story ideas and media enquiries, contact our Media team: