Dr Mahdi Abolghasemi

Lecturer

Mathematics
Faculty of Science

Overview

Dr. Abolghasemi has a multi-disciplinary background in Engineering, Business, Statistics, and Machine Learning. His research interests and expertise lie in time series forecasting, predictive analytics, decision making and machine learning, with applications in supply chain management and renewable energies optimisation. Through his research, Dr. Abolghasemi has consulted for dozens of companies in Australia, Europe, and the Middle East, developing several analytical models to enhance their supply chain networks and analytical capabilities. His research is closely tied to evidence-based analytics for real-world problems. He has published over 40 articles and technical reports and has presented at numerous national and international conferences.

Mahdi is a consultant and a member of the International Institute of Forecasters, the Australian Mathematical Society, and INFORMS. He serves on the editorial board of the International Journal of Forecasting and is a regular reviewer for the International Journal of Production Economics, the International Journal of Production Research, and several international conferences. Dr. Abolghasemi is the founding chair and host of an international scientific podcast, "Forecasting Impact," which reaches audiences in over 120 countries around the world.

Dr. Abolghasemi is a passionate teacher and thought leader in higher education. He has studied and worked on three continents. He dedicates himself to supervising talented young students, aiding them in achieving their goals.

Research Interests

  • Data Science
    How to extract useful information and hidden patterns in data and use them for prediction using advanced statistical and machine learning algorithms, developing predictive analytics algorithms
  • Time series forecasting
    Exploring time series and stream data for forecasting short-term and long terms behaviour of systems, identifying trends seasonality, spikes and troughs.
  • Applied Machine Learning
    How to use AI and machine learning for automating the process, and how to build machine learning production systems.
  • Optimisatoin and Decision Making
    How we can develop models to make optimal decisions based on mathematical models and machine learning, and how humans can use the information and make reliable decisions by judgment, building decision-support systems.

Research Impacts

Dr Mahdi Abolghasemi is passionate about applied research, i.e, research with applications in solving real-world problems. Dr Abolghasemi has worked on several industry research projects during his working experience in the industry and academic journey. His research has been used in several sectors including the food and beverage supply chain, renewable energy, automotive, mining and construction industries. Mahdi has developed and optimised several decision-making models that are currently in use by organisations in Australia, Europe, and Middle-East.

Publications

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Grants

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Supervision

  • Master Philosophy

  • Doctor Philosophy

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Available Projects

  • Predictive analytics and optimisation are two prominent techniques capable of addressing numerous real-world challenges. The "predict and optimise" paradigm refers to real-world problems where we need to first predict the unknown values of a variable and then optimise some decisions. For instance, one might aim to predict product demand to fine-tune production planning, or forecast electricity demand to optimally schedule machine operations. This approach has manifold applications across sectors like finance, retail, manufacturing, and energy. Within this context, predictions serve as inputs to optimisation models. While heightened prediction accuracy often bolsters optimisation, it doesn't always directly lead to enhanced results. The core challenge we seek to address is the seamless integration of these two phases to craft an end-to-end model that is focused on decision optimisation. Throughout this process, you will hone machine and deep learning models that consider final decisions in their forecasting efforts.

  • Probabilistic forecasting associates a probability of occurrence with the predicted values, making it a useful technique for determining decisions based on the level of risk one can take. It is a powerful technique that unlike point forecast gives you a complete view of the future unknown values. In this project, we aim to use Bayesian approaches for probabilistic forecasting to predict the demand for products/services and accordingly determine a better decision whatever that may be, e.g., inventory of product, or optimal allocation of resources. We investigate the association between these two using real-world data.

  • Hierarchical Forecasting has found many applications in real world. Hierarchical time series refers to a collection of time series that have a natural and structural connection, e.g, time series are gathered across different locations such as sales across different stores and states in a country. Research shows that we can leverage the information on sales in one store in a particular location and use that to forecast the sales for another store. This is known as cross-learning in research. This project aims to use optimisation methods like linear and integer programming in the setting of hierarchical time series, to develop an end-to-end algorithm that is able to forecast the entire series in an optimal way.

View all Available Projects

Publications

Book Chapter

Journal Article

Conference Publication

Grants (Administered at UQ)

PhD and MPhil Supervision

Current Supervision

Possible Research Projects

Note for students: The possible research projects listed on this page may not be comprehensive or up to date. Always feel free to contact the staff for more information, and also with your own research ideas.

  • Predictive analytics and optimisation are two prominent techniques capable of addressing numerous real-world challenges. The "predict and optimise" paradigm refers to real-world problems where we need to first predict the unknown values of a variable and then optimise some decisions. For instance, one might aim to predict product demand to fine-tune production planning, or forecast electricity demand to optimally schedule machine operations. This approach has manifold applications across sectors like finance, retail, manufacturing, and energy. Within this context, predictions serve as inputs to optimisation models. While heightened prediction accuracy often bolsters optimisation, it doesn't always directly lead to enhanced results. The core challenge we seek to address is the seamless integration of these two phases to craft an end-to-end model that is focused on decision optimisation. Throughout this process, you will hone machine and deep learning models that consider final decisions in their forecasting efforts.

  • Probabilistic forecasting associates a probability of occurrence with the predicted values, making it a useful technique for determining decisions based on the level of risk one can take. It is a powerful technique that unlike point forecast gives you a complete view of the future unknown values. In this project, we aim to use Bayesian approaches for probabilistic forecasting to predict the demand for products/services and accordingly determine a better decision whatever that may be, e.g., inventory of product, or optimal allocation of resources. We investigate the association between these two using real-world data.

  • Hierarchical Forecasting has found many applications in real world. Hierarchical time series refers to a collection of time series that have a natural and structural connection, e.g, time series are gathered across different locations such as sales across different stores and states in a country. Research shows that we can leverage the information on sales in one store in a particular location and use that to forecast the sales for another store. This is known as cross-learning in research. This project aims to use optimisation methods like linear and integer programming in the setting of hierarchical time series, to develop an end-to-end algorithm that is able to forecast the entire series in an optimal way.

  • Outlier detection is an important problem in many fields including in time series forecasting. Applications include detecting large spikes in transactions, or security breaches. There are some standard techniques that can be used for the early detection of outliers, e.g. extreme value theory.

    This research project explores the application of machine learning techniques in the fields of cybersecurity forecasting and anomaly detection. With the ever-growing sophistication of cyber threats, traditional security measures are often insufficient to protect systems and networks effectively. By leveraging machine learning algorithms, this study aims to develop accurate and efficient models for predicting cyber attacks and identifying anomalous behavior. The project involves analyzing large datasets of historical cybersecurity incidents, extracting relevant features, and training models to recognize patterns indicative of malicious activities. The findings of this research have the potential to enhance proactive cybersecurity measures and bolster defence mechanisms against evolving cyber threats.

  • Effective methods for sound classification are widely published, but these works often reference highly curated datasets or are applied to tightly controlled scenarios. Accurate sound classification in real-world environments are confounded by variability in signal-to-noise ratios and variability in the characteristics of noise sources. This work seeks to explore the influence that data representations (i.e. data engineering) and construction of training algorithms may have on the performance of environmental noise classification. An existing classification framework and training dataset are available for the purposes of baselining ‘existing’ performance.

    Expected Outcomes

    Improved understanding of the influence that algorithms and data representations have on the performance of noise classification problems.

    This is an industry-supported project. The interested student will work closely with Advitech.

    Suitable for Honours students.