Areas of expertise: Applied AI/ML, AI-enabled decision support systems, time series analytics (feature selection, prediction), sensor data analytics, information fusion, transfer learning.
Email: mashud.rana@acu.edu.au
ORCID ID: 0000-0003-2999-9367
HDR Supervisor accreditation status: Full
I am a Senior Lecturer in Computer Science at the Australian Catholic University (ACU), specialising in Artificial Intelligence (AI) and Machine Learning (ML) with a strong focus on developing innovative, real-world solutions across cross-disciplinary domains. I teach courses in Machine Learning, Artificial Intelligence, and Data Mining. I have successfully supervised several HDR and Honours students, with projects spanning deep learning for time-series analysis, sensor data fusion, spatial-temporal modelling, and transfer learning.
Before joining ACU, I served as a Senior Research Scientist at CSIRO's Data61 (2018-2024), where I led nationally significant projects applying AI/ML to pressing challenges in energy, health, and environmental systems. In 2025, I was appointed Assistant Director (Data Science) at the Australian Government's Asbestos and Silica Safety and Eradication Agency, leading AI governance and advanced analytics initiatives to inform policy and public health risk management.
Earlier in my career, I held Research Fellowships at the University of New South Wales (2015-2016) and the University of Sydney (2016-2018), where I also completed my PhD in Computer Science.
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Journal Papers
- S Almaghrabi, M Rana, M Hamilton, M S Rahaman, Multidimensional Dynamic Attention for Multivariate Time Series Forecasting, Applied Soft Computing, vol 167 (B), 2024. Link
- M Rana, A Rahman, D Smith, Hierarchical Semi-Supervised Approach for Classifying Activities of Workers Utilising Indoor Trajectory Data, Internet of Things, 2024, vol 28. Link
- S Almaghrabi, M Rana, M Hamilton, M S Rahaman, Multivariate solar power time series forecasting using multilevel data fusion and deep neural networks, Information Fusion, vol 104, 102180, 2024. Link
- S Almaghrabi, M Rana, M Hamilton, M S Rahaman, Solar power time series forecasting utilising wavelet coefficients, Neurocomputing, vol 508, pp 182-207, 2024. Link
- M Rana, S Sethuvenkatraman, R Heidari, S Hands, Solar thermal generation forecast via deep learning and application to buildings cooling system control, Renewable Energy, vol 196, pp 694-706, 2024. Link
- M Rana, A Rahman, J Dabrowski, S Arnold, J McCulloch, B Pais, Machine learning approach to investigate the influence of water quality on aquatic livestock in freshwater ponds, Biosystems Engineering, vol 208, pp 164-175, 2021. Link
- M Rana, S Sethuvenkatraman, M Goldsworthy, A data-driven approach based on quantile regression forest to forecast cooling load for commercial buildings, Sustainable Cities and Society, vol 76, 103511, 2021. Link
- A Rahman, M Xi, J Dabrowski, J McCulloch, S Arnold, M Rana, M Adcock,An integrated framework of sensing, machine learning, and augmented reality for aquaculture prawn farm management, Aquacultural Engineering, vol 95, 102192, 2021. Link
- M Rana, A Rahman, D Hugo, J McCulloch, A Hellicar, Investigating data-driven approaches to understand the interaction between water quality and physiological response of sentinel oysters in natural environment, Computers and Electronics in Agriculture, vol 175, 105545, 2020. Link
- M Rana, A Rahman, Multiple steps ahead solar photovoltaic power forecasting based on univariate machine learning models and data re-sampling, Sustainable Energy, Grids and Networks, vol 21, 100286, 2020. Link
- M Rana, I Koprinska, Neural network ensemble based approach for 2D-interval prediction of solar photovoltaic power, Energies, vol 9(10), 2016. Link
- M Rana, I Koprinska, V G Agelidis, Univariate and multivariate methods for very short-term solar photovoltaic power forecasting, Energy Conversion and Management, vol 121, pp 380–390, 2016. Link
- M Rana, I Koprinska, Forecasting electricity load with advanced wavelet neural networks. Neurocomputing, vol 182, pp 118-132, 2016. Link
- M Rana, I Koprinska, V G Agelidis, Correlation and instance based feature selection for electricity load forecasting, Knowledge Based Systems, vol 82, pp 29–40, 2015. Link
- M Rana, I Koprinska, V G Agelidis, 2D-interval forecasts for solar power production, Solar Energy, vol 122, pp 191-203–40, 2015. Link
Conference Papers
- A Rahman, M Rana, M Almashor, and J McCulloch, Inferring Sensor Metadata Based on Machine Learning for Portable Building Applications, in the proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) workshop on Workshop: Advanced Data-Driven Techniques for Urban Resilience, 2025. Link
- M Rana, A Rahman, M Almashor, J McCulloch, S Sethuvenkatraman, Automatic classification of sensors in buildings: learning from time series data, in the proceedings of the Australasian Joint Conference on Artificial Intelligence (AJCAI), 2023. Link
- M Almashor, M Rana, A Rahman, J McCulloch, S Sethuvenkatraman,What’s The Point: AutoEncoding Building Point Names, in the proceedings of the ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys), 2023. Link
- J Dabrowski, A Rahman, A Helicar, M Rana, S Arnold, Deep learning for prawn farming: forecasting and anomaly detection, in the proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2022. Link
- Y Lin, I Koprinska, M Rana, SSDNet: State space decomposition neural network for time series forecasting, in the proceedings of the IEEE Intrernational Conference on Data Mining (ICDM), 2021. Link
- S Almaghrabi, M Rana, M Hamilton, M S Rahaman, Forecasting regional level solar power generation using advanced deep learning approach, in the proceedings of the International Joint Conference on Neural Networks (IJCNN), 2021. Link
- S Almaghrabi, M Rana, M Hamilton, M S Rahaman, Spatially aggregated photovoltaic power prediction using wavelet and convolutional neural networks, in the proceedings of IJCNN, 2021. Link
- Y Lin, I Koprinska, M Rana, Temporal convolutional attention neural networks for time series forecasting, in the proceedings of IJCNN, 2021. Link
- Y Lin, I Koprinska, M Rana, SpringNet: Transformer and Spring DTW for time series fore- casting, in the proceedings of the International Conference on Neural Information Processing (ICONIP), 2020. Link
- M Rana, A Rahman, J Jin, A data-driven approach for forecasting state level aggregated solar photovoltaic power production, in the proceedings of IEEE WCCI (IJCNN), 2020. Link
- Y Lin, I Koprinska, M Rana, Temporal convolutional neural networks for solar power fore- casting, in the proceedings of IEEE WCCI (IJCNN), 2020. Link
- Y Lin, I Koprinska, M Rana, A Troncoso, Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-Learning, in the proceedings of the International Conference on Artificial Neural Networks (ICANN), 2020. Link
- Y Lin, I Koprinska, M Rana, A Troncoso, Pattern sequence neural network for solar power forecasting, in the proceedings of ICONIP, 2020. Link
Recent Projects
Sensor Data Analytics: This project focuses on developing an AI-driven system to automatically categorise sensors in smart buildings and map their semantic relationships by analysing unstructured text and time-series data using Large Language Models (LLM). The outcomes will enable large-scale interoperability of energy analytics applications across millions of buildings, unlocking significant gains in energy efficiency and operational performance.
AI for Health: In collaboration with the Westmead Institute for Medical Research (WIMR), this project develops advanced machine learning models to predict adverse outcomes in preterm infants. It leverages complex, large-scale unstructured clinical text and physiological time-series datasets from neonatal intensive care units (NICU).
Mental Health Analytics: This project applies cutting-edge machine learning and data science algorithms to identify the key drivers of physical violence and hospital readmissions among mental health patients in South Western Sydney Local Health District, Australia, providing insights that guided healthcare policy improvements to enhance patient outcomes.
Movement Analytics: A research and development initiative in activity recognition, this project designs predictive analytics solutions to detect production bottlenecks and enhance operational efficiency in manufacturing, using deep learning models on large-scale indoor mobility datasets.
HDR Supervision and Mentoring
- Sarah Almaghrabi, PhD (completed in 2023). Jointly supervised with Professor Margaret Hamilton and Dr Mohammad Saiedur Rahman. Project: Forecasting solar power time series: strategies for multi-modal data fusion, feature relevance, and sparse data management.
- Yang Lin, PhD (completed in 2022). Jointly supervised with Professor Irena Koprinska. Project: Deep learning for time series forecasting.
- Raymond Lou, Master by Research (completed in 2022). Jointly supervised with Professor Irena Koprinska. Project: Deep learning models for predicting solar thermal power generation.
Awards
- Best Publication Award - 2022 (Data61, CSIRO), for my technical report “Machine learning methods for identifying control schemes of residential battery installations using smart meter data” based on the research in the National Energy Analytics Research (NEAR) initiative.
- Research Excellence Award - 2022 and 2021 (Energy, CSIRO), for my research contributions to a project under NEAR initiative.
- Bronze Award at the Global AI Challenge for Building E&M Facilities, 2022, for developing a semantic ML model for forecasting energy demand in commercial buildings.
- Endeavour Postgraduate Award (2010-2014), funded by the Australian government for PhD studies at University of Sydney, Australia.
- Erasmus Mundus Scholarship (2009-2010), funded by the European Union for MS studies at University of Reading, UK.
- Chancellor’s Gold Medal and Vice-Chancellor’s Award, for outstanding academic achievements at Bachelor level examination, Shahjalal University of Science and Technology, Bangladesh (2007).
Appointments and Affiliations
Membership:
- ACM (Association for Computing Machinery)
- IEEE (Institute of Electrical and Electronics Engineers)
Invited Speech:
- ACM CIKM’22 Workshop on Applied Machine Learning Methods for Time Series Forecasting (AMLTS), 2022.
TPC Member:
- ICONIP’19-23, ICDM’21, IJCAI’21-22, PAKDD’16-18
Workshops Co-organiser:
- Ai2019 Workshop on ML for Agriculture
- Ai2018 Workshop on ML for Sensory Data Analytics
International journal review panels
- Reviewer: IEEE TNLS, IEEE TKDE, IEEE Access, Neurocomputing, Information Fusion, Applied Soft-Computing, Engineering Applications of AI, Applied Energy.
