1368648
Dr Mahdi Abolghasemi
Faculty of Science,
School of Mathematical Sciences
Biography
SummaryI am a Senior Lecturer in Statistical Data Science, specialising in time series forecasting, decision optimisation, and machine learning for retail and energy applications.
I have held academic positions at Monash University and The University of Queensland before joining Queensland University of Technology. I serve on the editorial board of the International Journal of Forecasting and I am a reviewer for multiple international journals including Scientific Reports, International Journal of Production Research, Applied Energy, Computers & Industrial Engineering.
Research
My research methodology integrates advanced statistical techniques with machine learning approaches to solve complex forecasting and optimization problems. I focus on developing novel algorithms that can handle hierarchical structures, systematic events, and volatility in time series data.
My primary research areas include demand forecasting in supply chains, energy forecasting for renewable sources, and decision-focused predictive analytics. I have received more than $900k funding for my research from national and international organisations. My work has been recognized with awards from International Institute of Forecasters, Australian Mathematical Society, IEEE Computational Intelligence Society, and Australian Mathematical Sciences Institute.
Teaching
I am a Fellow of Higher Education Academy (FHEA). My teaching philosophy centers on bridging theoretical concepts with practical applications, preparing students for real-world challenges in data science and analytics. I emphasize hands-on learning experiences and industry-relevant projects to foster critical thinking and problem-solving skills.
I have taught courses in data science, business analytics, time series forecasting, and statistical machine learning at both undergraduate and postgraduate levels. My commitment to teaching excellence has been recognized with awards such as the Faculty of Science Highly Regarded Early Career Teaching Excellence Award from The University of Queensland.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Personal details
Positions
- Senior Lecturer in Statistical Data Science
Faculty of Science,
School of Mathematical Sciences
Qualifications
- PhD in Statistics (University of Newcastle)
Professional memberships and associations
- International Institute of Forecasters
- Institute for Operations Research and Management Science (INFORMS)
- Australian Mathematical Society
Teaching
- Modern Computing Techniques (QUT S2-2024)
- Applications of Computational Statistics (University of Queensland)
- Statistical Methods in Business (University of Queensland)
- Machine Learning in Business (University of Queensland)
- Introduction to Data Science (Monash University)
- Business Modelling and Optimisation (Monash University)
Experience
I have worked with several entities in retail and energy sectors on forecasting and optimisation problems. I have worked with the following organisations in different capacities: Sanitarium, Coles and Woolworths, Australian Energy Market Operator (AEMO), Australian Renewable Energy Agency (ARENA), CSR , and Urban Utility.
Publications
Research outputs by year
- Abolghasemi, M., Ganbold, O. & Rotaru, K. (2025). Humans vs. large language models: Judgmental forecasting in an era of advanced AI. International Journal of Forecasting, 41(2), 631–648. https://eprints.qut.edu.au/256102
- Abolghasemi, M., Girolimetto, D. & Di Fonzo, T. (2025). Improving cross-temporal forecasts reconciliation accuracy and utility in energy market. Applied Energy, 394. https://eprints.qut.edu.au/259292
- Bergmeir, C., de Nijs, F., Genov, E., Sriramulu, A., Abolghasemi, M., Bean, R., Betts, J., Bui, Q., Dinh, N., Einecke, N., Esmaeilbeigi, R., Ferraro, S., Galketiya, P., Glasgow, R., Godahewa, R., Kang, Y., Limmer, S., Magdalena, L., Montero-Manso, P., Peralta, D., Kumar, Y., Rosales-Pérez, A., Ruddick, J., Stratigakos, A., Stuckey, P., Tack, G., Triguero, I. & Yuan, R. (2025). Predict+Optimize Problem in Renewable Energy Scheduling. IEEE Access, 13, 60064–60087. https://eprints.qut.edu.au/259291
- Abolghasemi, M., Abbasi, B. & HosseiniFard, Z. (2025). Machine learning for satisficing operational decision making: A case study in blood supply chain. International Journal of Forecasting, 41(1), 3–19. https://eprints.qut.edu.au/251243
- English, L. & Abolghasemi, M. (2024). Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure. Sustainable Energy, Grids and Networks, 40. https://eprints.qut.edu.au/256291
- Abolghasemi, M., Tarr, G. & Bergmeir, C. (2024). Machine learning applications in hierarchical time series forecasting: Investigating the impact of promotions. International Journal of Forecasting, 40(2), 597–615. https://eprints.qut.edu.au/251244
- Abolghasemi, M., (2023). The intersection of machine learning with forecasting and optimisation: theory and applications. In M. Hamoudia, S. Makridakis & E. Spiliotis (Eds.), Forecasting with Artificial Intelligence: Theory and Applications (pp. 313–339). Palgrave Macmillan. https://eprints.qut.edu.au/251398
- Abolghasemi, M., Hyndman, R., Spiliotis, E. & Bergmeir, C. (2022). Model selection in reconciling hierarchical time series. Machine Learning, 111(2), 739–789. https://eprints.qut.edu.au/251255
- Spiliotis, E., Abolghasemi, M., Hyndman, R., Petropoulos, F. & Assimakopoulos, V. (2021). Hierarchical forecast reconciliation with machine learning. Applied Soft Computing, 112. https://eprints.qut.edu.au/251256
- Abolghasemi, M., Beh, E., Tarr, G. & Gerlach, R. (2020). Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion. Computers and Industrial Engineering, 142. https://eprints.qut.edu.au/251252
QUT ePrints
For more publications by Mahdi, explore their research in QUT ePrints (our digital repository).
Filter publications:
A complete list of publications is available at: https://www.qut.edu.au/about/our-people/academic-profiles/mahdi.abolghasemi
Awards
- Type
- Editorial Role for an Academic Journal
- Reference year
- 2026
- Details
- Book Editor, Reviewer
- Type
- Funding Award
- Reference year
- 2026
- Details
- Awarded by International Institute of Forecasters and SAS for the project on Federated Hierarchical Learning
- Type
- Academic Honours, Prestigious Awards or Prizes
- Reference year
- 2025
- Details
- QUT Early Career Academic- Excellence in Research Collegiality 2025
- Type
- Fellowships
- Reference year
- 2023
- Details
- Fellow of Higher Education Academy
Supervision
Looking for a postgraduate research supervisor?
I am currently accepting research students for Honours, Masters and PhD study.
- Physics informed machine learning for energy forecasting
- Forecast stability and volatility control in decision-centric time series forecasting
- Multi-objective optimisation models for forecasting and decision-making in supply chains and energy systems
- Hierarchical forecasting: forecasting a collection of time series
- Bayesian focused learning
- Optimising inventory control and demand forecast accuracy though multi-objective optimisation
- Decision optimisation in energy supply chain
- Probabilistic forecasting of energy
- Spatio-Temporal Forecasting of renewable energies
You can browse existing student topics offered by QUT or propose your own topic.