Bahman Rostami-Tabar

Bahman Rostami-Tabar is Professor of Data-Driven Decision Science at Cardiff University (United Kingdom). His fellowship at the Montpellier Advanced Knowledge Institute on Transitions runs from 8 January 2024 to 30 June 2024.


Bahman is Professor of Data-Driven Decision Science at Cardiff Business School, Cardiff University (United Kingdom).

Bahman is the founder and director of Data Lab for Social Good Research Group at Cardiff University and the founder of Forecasting for Social Good committee at the International Institute of Forecasters. He is also leading the “Uncertainty & the Future” theme at the Digital Transformation Innovation Institute.

Bahman is passionate about uncertainty and the future, transforming data into insights for making better decisions in an uncertain future. He specialises in the development and application of probabilistic modelling, forecasting, and Operational Research tools and techniques, providing informed insights for policy & decision-making processes in facing uncertain futures, and his research has contributed to sectors contributing to social good, including healthcare operations, global health and humanitarian supply chains, agriculture and food, social sustainability, and governmental policy. 

Bahman’s collaborative efforts have spanned a multitude of organisations, including notable bodies such as the National Health Service (NHS), Welsh Ambulance Service Trusts (WAST), the United States Agency for International Development (USAID), the International Committee of the Red Cross (ICRC), John Snow Inc. (JSI), and the Ethiopian Pharmaceutical Supply Service (EPSS). A remarkable highlight of his contributions is his pivotal role in disseminating forecasting knowledge, especially in low and lower-middle income countries, through the democratizing forecasting project sponsored by the International Institute of Forecasters.

Bahman’s research contributions fall into three areas:

1) Conceptual, focusing on how forecasting and modelling can be used for social good or to inform decisions related to the Sustainable Development Goals (see, for example, Forecasting for social good; Alliance or apathy? Forecasting’s role in achieving the U.N. sustainable development goals; also, harm in forecasting and barriers in using models in healthcare

2) Methodological, Investigate methodological solutions to questions about theory and practice of how; see, for example, Forecasting interupted time series; Exploring the association between time series features and forecasting by temporal aggregation using machine learning; and Demand forecasting by temporal aggregation.

3) Applications, Application of forecasting and modelling in healthcare management science, global health, & humanitarian supply chains. See for example Hierarchical Time Series Forecasting in Emergency Medical Services; Probabilistic forecasting of hourly emergency department arrivals; and A hybrid LSTM method for forecasting demands of medical items in humanitarian operations.

Recent publications on Bahman’s ORCID profile include:

  • Rostami-Tabar, Bahman, Mohammad M. Ali, Tao Hong, Rob J. Hyndman, Michael D. Porter, and Aris Syntetos, ‘Forecasting for Social Good’, International Journal of Forecasting, 38.3 (2022), pp. 1245–57, doi:10.1016/j.ijforecast.2021.02.010
  • Rostami-Tabar, Bahman, Jethro Browell, and Ivan Svetunkov, ‘Probabilistic Forecasting of Hourly Emergency Department Arrivals’, Health Systems, 13.2 (2024), pp. 133–49, doi:10.1080/20476965.2023.2200526
  • Rostami-Tabar, Bahman, and Rob J. Hyndman, ‘Hierarchical Time Series Forecasting in Emergency Medical Services’, Journal of Service Research, 2024, p. 10946705241232169, doi:10.1177/10946705241232169