Staff Profile

Stephen McGough is a Senior Lecturer in Data Science and Machine Learning within the Scalable Computing Group in the School of Computing Science at Newcastle University. Stephen researches in the areas of Machine Learning (specifically Deep Learning), Big Data and Energy Efficient Computing.

Stephen studied parallel simulation (PhD, Newcastle) having previously completed an MSc in Computing (Newcastle) and a BSc in Mathematics (Durham).

Before joining Newcastle in 2017, he was a Associate Professor (Senior Lecturer) at Durham University (2013-2017), he managed the Digital Institute at Newcastle University (2009-2013), Senior Research Associate University College London (2009), and Senior Research Associate London e-Science Centre, Imperial College London (2000-2009).

You can find out more on what Stephen does on the NAIL website.

Google scholar: Click here.

Area of expertise: Big Data, Machine Learning, Deep Learning

• Khalil M, McGough AS, Pourmirza Z, Pazhoohesh M, Walker S. Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption — A systematic review. Engineering Applications of Artificial Intelligence 2022, 115, 105287.
• Koc K, McGough AS, Johansson Fernstad S. PeaGlyph: Glyph design for investigation of balanced data structures. Information Visualization 2022, 21(1), 74-92.
• Allen B, McGough AS, Devlin M. Toward a Framework for Teaching Artificial Intelligence to a Higher Education Audience. ACM Transactions on Computing Education 2022, 22(2), 1-29.
• Chen SH, Londono-Larrea P, McGough AS, Bible AN, Gunaratne C, Araujo-Granda PA, Morrell-Falvey JL, Bhowmik D, Fuentes-Cabrera M. Application of Machine Learning Techniques to an Agent-Based Model of Pantoea. Frontiers in Microbiology 2021, 12, 726409.
• Kell AJM, McGough AS, Forshaw M. The impact of online machine-learning methods on long-term investment decisions and generator utilization in electricity markets. Sustainable Computing: Informatics and Systems 2021, 30, 100532.
• Khalil M, McGough S, Pourmirza Z, Pazhoohesh M, Walker S. Transfer Learning Approach for Occupancy Prediction in Smart Buildings. In: 2021 12th International Renewable Engineering Conference (IREC). 2021, Amman, Jordan: IEEE.
• Allen B, Devlin M, McGough AS. Using the One Minute Paper to Gain Insight into Potential Threshold Concepts in Artificial Intelligence Courses. In: CEP '21: Proceedings of the 5th Conference on Computing Education Practice. 2021, Durham, UK (Online): ACM.
• Al Moubayed N, McGough S, Awwad Shiekh Hasan B. Beyond the topics: how deep learning can improve the discriminability of probabilistic topic modelling. PeerJ Computer Science 2020, 6, 1-32.
• Kell AJM, Salas P, Mercure JF, Forshaw M, McGough AS. Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs. In: Tackling Climate Change with Machine Learning workshop at NeurIPS 2020. 2020, Climate Change AI.
• Ullah MS, McGough AS. Distributed Disk Store. In: ICCA 2020 : International Conference on Computing Advancements. 2020, Dhaka, Bangladesh.
• Kell AJM, Forshaw M, McGough AS. Long-term electricity market agent based model validation using genetic algorithm based optimization. In: The Eleventh ACM International Conference on Future Energy Systems (e-Energy’20). 2020, Virtual, Melbourne, Australia: ACM.
• Trotter C, Atkinson G, Sharpe M, Richardson K, McGough S, Wright N, Burville B, Berggren P. NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation. arXiv 2020, 5.
• Brennan J, Bonner S, Atapour-Abarghouei A, Jackson PT, Obara B, McGough AS. Not Half Bad: Exploring Half-Precision in Graph Convolutional Neural Networks. In: 2020 IEEE International Conference on Big Data. 2020, Online: IEEE.
• Kell AJM, McGough AS, Forshaw M. Optimizing carbon tax for decentralized electricity markets using an agent-based model. In: The Eleventh ACM International Conference on Future Energy Systems (e-Energy’20). 2020, Virtual, Melbourne, Australia: ACM.
• Atapour-Abarghouei A, McGough AS, Wall DS. Resolving the cybersecurity Data Sharing Paradox to scale up cybersecurity via a co-production approach towards data sharing. In: 2020 IEEE International Conference on Big Data. 2020, Virtual: IEEE.
• Atapour Abarghouei A, Bonner S, McGough AS. A King’s Ransom for Encryption: Ransomware Classification using Augmented One-Shot Learning and Bayesian Approximation. In: 2019 IEEE International Conference on Big Data 2019. 2019, Los Angeles, California, USA: IEEE.
• Kell AJM, Forshaw M, McGough AS. ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning. In: Tenth ACM International Conference on Future Energy Systems (ACM e-Energy). 2019, Phoenix, AZ: ACM.
• Bonner S, Kureshi I, Brennan J, Theodoropoulos G, McGough AS, Obara B. Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study. Data Science and Engineering 2019, 4, 269-289.
• Gogulancea V, Gonzalez-Cabaleiro R, Li B, Taniguchi D, Jayathilake PG, Chen J, Wilkinson D, Swailes D, McGough S, Zuliani P, Ofiteru ID, Curtis T. Individual based model links thermodynamics, chemical speciation and environmental conditions to microbial growth. Frontiers in Microbiology 2019, 10, 1871.
• Kell AJM, Forshaw M, McGough AS. Modelling Carbon Tax in the UK Electricity Market using an Agent-Based Model. In: Tenth ACM International Conference on Future Energy Systems (e-Energy '19). 2019, Phoenix, AZ, USA: ACM.
• Li B, Taniguchi D, Pahala- Gedara J, Gogulancea V, Gonzalez-Cabaleiro R, Chen J, McGough AS, Ofiteru ID, Curtis TP, Zuliani P. NUFEB: A Massively Parallel Simulator for Individual-based Modelling of Microbial Communities. PLoS Computational Biology 2019, 15(2), e1007125.
• Mohammadi M, Jaf S, McGough AS, Breckon TP, Matthews P, Theodoropoulos G, Obara B. On the Use of Neural Text Generation for the Task of Optical Character Recognition. In: 16th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2019). 2019, Abu Dhabi, UAE: IEEE.
• Kell AJM, Forshaw M, McGough AS. Optimising energy and overhead for large parameter space simulations. In: The Tenth International Green and Sustainable Computing Conference (IGSC). 2019, Alexandria, VA, U.S.A: IEEE.
• Alrajeh O, Forshaw M, McGough AS, Thomas N. Simulation of Virtual Machine Live Migration in High Throughput Computing Environments. In: IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT). 2019, Madrid: IEEE.
• Bonner S, Atapour Abarghouei A, Jackson P, Brennan J, Kureshi I, Theodoropoulos G, McGough S, Obara B. Temporal Neighbourhood Aggregation: Predicting Future Links in Temporal Graphs via Recurrent Variational Graph Convolutions. In: IEEE International Conference on Big Data (Big Data 2019). 2019, Los Angeles, CA, USA: IEEE.
• Trotter C, Atkinson G, Sharpe M, McGough AS, Wright N, Berggren P. The Northumberland Dolphin Dataset: A Multimedia Individual CetaceanDataset for Fine-Grained Categorisation. In: The 6th Workshop on Fine-Grained Visual Categorization Computer Vision and Pattern Recognition (CVPR). 2019, New Orleans, LA, USA.
• Atapour Abarghouei A, Bonner S, McGough AS. Volenti non fit injuria: Ransomware and its Victims. In: 2019 IEEE International Conference on Big Data 2019. 2019, Los Angeles, California, USA: IEEE.
• Gajbhiye A, Jaf S, Al Moubayed N, McGough AS, Bradley S. An exploration of dropout with RNNs for natural language inference. In: 27th International Conference on Artificial Neural Networks (ICANN 2018). 2018, Rhodes, Greece: Springer.
• Ryder T, Golightly A, McGough AS, Prangle D. Black-box Variational Inference for Stochastic Differential Equations. In: 35th International Conference on Machine Learning (ICML 2018). 2018, Stockholm, Sweden: International Machine Learning Society.
• Gajbhiye A, Jaf J, Al Moubayed N, Bradley S, McGough AS. CAM: A Combined Attention Model for Natural Language Inference. In: IEEE International Conference on Big Data. 2018, Seattle, WA, USA: IEEE.
• Silva PMP, Forshaw M, McGough AS. Clustering Vehicles based on Trips Identified from Automatic Number Plate Recognition Camera Scans. In: 1st International Workshop on Big Traffic Data Analytics (BigTraffic 2018). 2018, San Diego, California, USA.
• McGough AS, Forshaw M. Evaluation of Energy Consumption of Replicated Tasks in a Volunteer Computing Environment. In: 4th International Workshop on Energy-aware Simulation (ENERGY-SIM’18). 2018, Berlin: ACM.
• McGough AS, Forshaw M. Introduction to special issue on Energy-Aware Simulation and Modelling (ENERGY-SIM). Sustainable Computing: Informatics and Systems 2018, 18, 135-136.
• Justus D, Brennan J, Bonner S, McGough AS. Predicting the Computational Cost of Deep Learning Models. In: IEEE International Conference on Big Data. 2018, Seattle, WA, USA: IEEE.
• Kell A, McGough AS, Forshaw M. Segmenting Residential Smart Meter Data for Short-Term Load Forecasting. In: Proceedings of the Ninth International Conference on Future Energy Systems (e-Energy '18). 2018, Karlsruhe, Germany: ACM.
• Alhassan Z, McGough AS, Alshammari R, Daghstani T, Budgen D, Al-Moubayed N. Stacked Denoising Autoencoders for mortality risk prediction using imbalanced clinical data. In: IEEE 17th International Conference on Machine Learning and Applications (ICMLA 2018). 2018, Orlando, Fl, USA: IEEE.
• Bonner S, Brennan J, Kureshi I, Theodoropoulos G, McGough AS, Obara B. Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning. In: IEEE International Conference on Big Data. 2018, Seattle, WA, USA: IEEE.
• Medhat F, Mohammadi M, Jaf S, Willcocks CG, Breckon TP, Matthews P, McGough AS, Theodoropoulos G, Obara B. TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text. In: IEEE International Conference on Big Data (Big Data 2018). 2018, Seattle, WA, USA: IEEE.
• Alhassan Z, McGough AS, Alshammari R, Daghstani T, Budgen D, Al Moubayed N. Type-2 Diabetes Mellitus diagnosis from time series clinical data using Deep Learning models. In: 27th International Conference on Artificial Neural Networks (ICANN 2018). 2018, Rhodes, Greece: Springer.
• McGough AS, Forshaw M, Brennan J, Al Moubayed N, Bonner S. Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments. In: 9th International Green and Sustainable Computing Conference (IGSC 2018). 2018, Pittsburg, PA, USA: IGSC.
• Jayathilake PG, Gupta P, Li B, Madsen C, Oyebamiji O, Gonzalez-Cabaleiro R, Rushton S, Bridgens B, Swailes D, Allen B, McGough AS, Zuliani P, Ofiteru ID, Wilkinson DJ, Chen J, Curtis TP. A mechanistic individual-based model of microbial communities. PLoS One 2017, 12(8), e0181965.
• Al Moubayed N, Awwad Shiekh Hasan B, McGough AS. Enhanced Detection of Movement Onset in EEG through Deep Oversampling. In: International Joint Conference on Neural Networks (IJCNN). 2017, Anchorage, AK, USA: IEEE.
• Bonner S, Brennan J, Kureshi I, Theodoropoulos G, McGough AS, Obara B. Evaluating the quality of graph embeddings via topological feature reconstruction. In: 2017 IEEE International Conference on Big Data. 2017, Boston, MA, USA: IEEE.
• Al Moubayed N, Wall D, McGough AS. Identifying Changes in the Cybersecurity Threat Landscape Using the LDA-Web Topic Modelling Data Search Engine. In: 5th International Conference on Human Aspects of Information Security, Privacy, and Trust (HAS 2017). 2017, Vancouver: Springer.
• McGough AS, Moubayed NA, Forshaw M. Using Machine Learning in Trace-driven Energy-Aware Simulations of High-Throughput Computing Systems. In: ICPE 2017 Companion: Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering. 2017, L'Aquila, Italy: ACM.
• Bonner S, Brennan J, Theodoropoulos G, Kureshi I, McGough AS. Deep Topology Classification: A New Approach for Massive Graph Classification. In: 2016 IEEE International Conference on Big Data (Big Data). 2016, Washington, DC: IEEE.
• Bonner S, Brennan J, Kureshi I, McGough AS. Efficient Comparison of Massive Graphs Through The Use Of 'Graph Fingerprints'. In: 12th International Workshop on Mining and Learning with Graphs. 2016, San Francisco.
• Bonner S, Brennan J, Theodoropoulos G, Kureshi I, McGough AS. GFP-X: A Parallel Approach To Massive Graph Comparison Using Spark. In: 2016 IEEE International Conference on Big Data (Big Data). 2016, Washington, DC: IEEE.
• Forshaw M, McGough AS, Thomas N. HTC-Sim: a trace-driven simulation framework for energy consumption in high-throughput computing systems. Concurrency and Computation: Practice and Experience 2016, 28(12), 3260-3290.
• Al Moubayed N, Breckon T, Matthews P, McGough AS. SMS Spam Filtering Using Probabilistic Topic Modelling and Stacked Denoising Autoencoder. In: 25th International Conference on Artificial Neural Networks (ICANN 2016). 2016, Barcelona: Springer.
• Forshaw M, McGough AS, Thomas N. The case for energy-aware simulation and modelling of internet of things (IoT). In: 2nd International Workshop on Energy-Aware Simulation. 2016, Waterloo, Ontario, Canada: ACM Digital Library.
• Bonner S, McGough AS, Kureshi I, Brennan J, Theodoropoulos G, Moss L, Corsar D, Antoniou G. Data quality assessment and anomaly detection via map/reduce and linked data: a case study in the medical domain. In: 2015 IEEE International Conference on Big Data (Big Data). 2015, Santa Clara, CA, USA: IEEE.
• McGough AS, Arief B, Gamble C, Wall D, Brennan J, Fitzgerald J, van Moorsel A, Alwis S, Theodoropoulos G, Ruck-Keene E. Detecting insider threats using ben-ware: Beneficial intelligent software for identifying anomalous human behaviour. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 2015, 6(4), 3-46.
• McGough AS, Forshaw M. Energy-aware simulation of workflow execution in High Throughput Computing systems. In: 19th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications (DS-RT). 2015, Chengdu, China: IEEE/ACM.
• Forshaw M, McGough AS, Thomas N. Energy-efficient Checkpointing in High-throughput Cycle-stealing Distributed Systems. Electronic Notes in Theoretical Computer Science 2015, 310, 65-90.
• Forshaw M, McGough AS. Flipping the priority: effects of prioritising HTC jobs on energy consumption in a multi-use cluster. In: ENERGY-SIM 2015. 2015, Athens, Greece.
• McGough AS, Wall D, Brennan J, Theodoropoulos G, Ruck-Keene E, Arief B, Gamble C, Fitzgerald F, van Moorsel A, Alwis S. Insider Threats: Identifying Anomalous Human Behaviour in Hereogeneous Systems Using Beneficial Intelligent Software (Ben-ware). In: 7th ACM CCS International Workshop on Managing Insider Security Threats. 2015, Denver, CO, USA: ACM.
• McGough AS, Forshaw M, Gerrard C, Wheater S, Allen B, Robinson P. Comparison of a cost-effective virtual cloud cluster with an existing campus cluster. Future Generation Computer Systems 2014, 41, 65-78.
• Forshaw M, McGough AS, Thomas N. Energy-efficient checkpointing in high-throughput cycle-stealing distributed systems. In: Seventh International Workshop on Practical Applications of Stochastic Modelling (PASM). 2014, Newcastle upon Tyne, UK: Elsevier BV.
• Forshaw M, McGough AS, Thomas N. On energy-efficient checkpointing in high-throughput cycle-stealing distributed systems. In: SMARTGREENS 2014 - Proceedings of the 3rd International Conference on Smart Grids and Green IT Systems. 2014, Barcelona, Spain: SciTePress.
• Forshaw M, McGough AS, Thomas N. On Energy-efficient Checkpointing in High-throughput Cycle-stealing Distributed Systems. In: 3rd International Conference on Smart Grids and Green IT Systems (SMARTGREENS) 2014. 2014, Barcelona.
• McGough AS, Mitrani I. Optimal Hiring of Cloud Servers. In: European Performance Engineering Workshop. 2014, Florence: Springer.
• McGough AS, Forshaw M. Reduction of wasted energy in a volunteer computing system through Reinforcement Learning. Sustainable Computing: Informatics and Systems 2014, 4(4), 262-275.
• Forshaw M, Thomas N, McGough AS. Trace-Driven Simulation for Energy Consumption in High Throughput Computing Systems. In: IEEE/ACM 18th International Symposium on Distributed Simulation and Real Time Applications. 2014, Toulouse, France: IEEE.
• McGough AS, Forshaw M, Gerrard C, Robinson P, Wheater S. Analysis of power-saving techniques over a large multi-use cluster with variable workload. Concurrency and Computation: Practice and Experience 2013, 25(18), 2501-2522.
• Glenis V, McGough AS, Kutija V, Kilsby C, Woodman S. Flood modelling for cities using Cloud computing. Journal of Cloud Computing: Advances, Systems and Applications 2013, 2(1), 7.
• Glenis V, Kutija V, Kilsby C, McGough AS, Woodman S. Urban flood modelling using cloud computing. In: International Conference on Flood Resilience Experiences in Asia and Europe (ICFR 2013). 2013, Exeter, UK.
• McGough AS, Liang S, Rapoportas M, Grey R, Vinod GK, Maddy D, Trueman A, Wainwright J. Massively parallel landscape-evolution modelling using general purpose graphical processing units. In: 19th International Conference on High Performance Computing (HiPC 2012). 2012, Pune: IEEE.
• McGough AS, Forshaw M, Gerrard C, Wheater S. Reducing the number of miscreant tasks executions in a multi-use cluster. In: Second International Conference on Cloud and Green Computing (CGC). 2012, Xiangtan: IEEE.
• Mcgough AS, Turner M, Mortimer D, Woodman S, Watson P. The Cutting Edge: Discoverability for Historical Artefacts. In: Microsoft e-Science Workshop. 2012, Chicago, Illinois, USA.
• McGough AS, Gerrard C, Noble J, Robinson P, Wheater S. Analysis of Power-Saving Techniques over a large multi-use Cluster. In: International Conference on Cloud and Green Computing (CGC2011). 2011, Sydney, Australia: IEEE.
• McGough S, Gerrard C, Haldane P, Sharples D, Swan D, Robinson P, Hamlander S, Wheater S. Intelligent Power Management over Large Clusters. In: Green Computing and Communications (GreenCom). 2010, Hangzhou, Zhejiang, China: IEEE.
• McGough AS, Mitrani I. Efficient parallel simulation of a sliding window protocol. Performance Evaluation 2002, 48(1-4), 237-246.
• McGough AS, Mitrani I. Efficient distributed simulation of a communication switch with bursty sources and losses. In: 14th Workshop on Parallel and Distributed Simulation. 2000, University of Bologna, Italy: IEEE.
• McGough SA, Mitrani I. Efficient Parallel Simulation of a Sliding Window Protocol. In: 7th IFIP International Conference on ATM Networks, Ilkley, UK, 2000. 2000.
• McGough AS, Mitrani I. Parallel simulation of ATM switches using relaxation. Performance Evaluation 2000, 41(2), 149-164.
• McGough AS, Mitrani I. Parallel Simulation of ATM Switches Using Relaxation. Performance Evaluation (Special Issue on ATM Networks: Performance Modelling and Analysis) 2000, 41(2-3), 149-164.
• McGough AS. Parallel simulations using recurrence relations and relaxation [PhD thesis]. Newcastle upon Tyne: University of Newcastle upon Tyne, 2000. Department of Computing Science.
• McGough SA, Mitrani I. Parallel Simulation of ATM Switches Using Relaxation. In: 6th IFIP International Conference on ATM Networks (IFIP ATM). 1998, Ilkley, West Yorkshire, UK.