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Professor Bahman Rostami-Tabar
(he/him)
- Available for postgraduate supervision
Teams and roles for Bahman Rostami-Tabar
Professor
Overview
Bahman is the Professor of Analytics & Decision Sciences at Cardiff Business School, Cardiff University, UK. He is interested in transforming data into insights for making better decisions. Bahman is the founder and director of Data Lab for Social Good Research Group at Cardiff Business School and the founder and chair of Forecasting for Social Good initiatives sponsored by 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. 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 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 (in-progress) and barriers in using models in healthcare (in-progress)
2) Methodological, Investigate methodological solutions to questions about philosophy, theory and practice; 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, and UN Sustainable Development Goals. 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.
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), and John Snow Inc. (JSI). 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.
Publication
2026
- Pinson, P. et al., 2026. Editorial: Reproducibility at the International Journal of Forecasting. International Journal of Forecasting (10.1016/j.ijforecast.2026.08.004)
- Halgamuwe Hewage, H. and Rostami-Tabar, B. 2026. A hybrid predictive and prescriptive modelling framework for long-term mental healthcare workforce planning. Journal of the Operational Research Society 77 (9), pp.2529-2553. (10.1080/01605682.2025.2573825)
- Gui, E. M. et al., 2026. What makes the contribution of science towards a sustainable future so difficult? A controversy analysis. Humanities & Social Sciences Communications 13 476. (10.1057/s41599-026-06642-0)
- Birhanu, S. et al., 2026. The role of technology in developing resilient supply chains: A systematic literature review during the COVID-19 pandemic and the disruptions of economic sanctions. Journal of Humanitarian Logistics and Supply Chain Management 16 (1), pp.27-49. (10.1108/JHLSCM-03-2024-0036)
2025
- Wang, Z. et al. 2025. A systematic literature review of trauma systems: An operations management perspective. Advances in Rehabilitation Science and Practice 2024 14. (10.1177/27536351241310645)
- Bilal, A. I. et al., 2025. The missing puzzle piece: contextual insights for enhanced pharmaceutical supply chain forecasting. International Journal of Production Research 63 (24), pp.10189-10207. (10.1080/00207543.2025.2546028)
- Lentlea, D. et al., 2025. Using simulation for long-term bed modelling in critical care. Journal of Simulation 19 (6), pp.657-673. (10.1080/17477778.2024.2412009)
- Omri, O. et al., 2025. On the inventory performance of hierarchical forecasting approaches: case of a vaccine supply chain. Journal of the Operational Research Society (10.1080/01605682.2025.2566323)
- Shi, M. , Rostami-Tabar, B. and Gartner, D. 2025. Looking for the crystal ball in unscheduled care: A systematic literature review of the forecasting process. Health Care Management Science 28 , pp.548-564. (10.1007/s10729-025-09711-z)
- Wang, Z. et al. 2025. Patient length of stay in trauma networks: Insights from advanced modelling. Expert Systems with Applications 289 127801. (10.1016/j.eswa.2025.127801)
- Halagmuwe Hewage, H. et al. 2025. A novel hybrid approach to contraceptive demand forecasting: integrating point predictions with probabilistic distributions. International Journal of Production Research (10.1080/00207543.2025.2526169)
- Rostami-Tabar, B. and Hyndman, R. J. 2025. Hierarchical time series forecasting in emergency medical services. Journal of Service Research 28 (2), pp.278-295. (10.1177/10946705241232169)
2024
- Wang, Z. et al. 2024. Investigating length of stay patterns and its predictors in the South Wales Trauma Network. Advances in Rehabilitation Science and Practice 13 (10.1177/27536351241237866)
- Hasni, M. , Babai, M. Z. and Rostami-Tabar, B. 2024. A hybrid LSTM method for forecasting demands of medical items in humanitarian operations. International Journal of Production Research 62 (17), pp.6046-6063. (10.1080/00207543.2024.2306904)
- Hyndman, R. J. and Rostami-Tabar, B. 2024. Forecasting interrupted time series. Journal of the Operational Research Society (10.1080/01605682.2024.2395315)
- Rostami-Tabar, B. , Browell, J. and Svetunkov, I. 2024. Probabilistic forecasting of hourly Emergency Department arrivals. Health Systems 13 (2), pp.133-149. (10.1080/20476965.2023.2200526)
- Rostami-Tabar, B. , Porter, M. D. and Pinson, P. 2024. Guest editorial: Forecasting for social good. International Journal of Forecasting 41 , pp.1-2. (10.1016/j.ijforecast.2024.08.007)
- Rostami-Tabar, B. et al. 2024. Probabilistic forecasting of daily COVID-19 admissions using machine learning. IMA Journal of Management Mathematics 35 (1), pp.21-43. (10.1093/imaman/dpad009)
- Rostami-Tabar, B. and Gilliland, M. 2024. Alliance or apathy? Forecasting’s role in achieving the U.N. sustainable development goals. Foresight: The International Journal of Applied Forecasting (74), pp.5-6.
2023
- Babaveisi, V. et al., 2023. Integrated demand forecasting and planning model for repairable spare part: an empirical investigation. International Journal of Production Research 61 (20), pp.6791-6807. (10.1080/00207543.2022.2137596)
- Gartner, D. et al. 2023. Challenging the throwaway culture in hospitals: scheduling the mix of reusable and single-use bronchoscopes. Journal of the Operational Research Society 74 (10), pp.2215-2226. (10.1080/01605682.2022.2129490)
- Kolassa, S. , Rostami-Tabar, B. and Siemsen, E. 2023. Demand forecasting for executives and professionals. Routledge Taylor & Francis Group. (10.1201/9781003399599)
- Rostami-Tabar, B. and Mircetic, D. 2023. Exploring the association between time series features and forecasting by temporal aggregation using machine learning. Neurocomputing 548 126376. (10.1016/j.neucom.2023.126376)
- Rostami-Tabar, B. , Babai, Z. and Syntetos, A. 2023. To aggregate or not to aggregate: Forecasting of finite autocorrelated demand. Journal of the Operational Research Society 74 (8), pp.1840-1859. (10.1080/01605682.2022.2118631)
- Abolghasemi, M. , Rostami-Tabar, B. and Syntetos, A. 2023. The value of point of sales information in upstream supply chain forecasting: an empirical investigation. International Journal of Production Research 61 (7), pp.2162-2177. (10.1080/00207543.2022.2063086)
- Rostami-Tabar, B. and Disney, S. 2023. On the order-up-to policy with intermittent integer demand and logically consistent forecasts. International Journal of Production Economics 257 108763. (10.1016/j.ijpe.2022.108763)
- Rostami-Tabar, B. , Goltsos, T. E. and Shixuan, W. 2023. Forecasting for lead-time period by temporal aggregation: Whether to combine and how. Computers in Industry 145 103803. (10.1016/j.compind.2022.103803)
2022
- Rostami-Tabar, B. , Hasni, M. and Babai, Z. 2022. On the inventory performance of demand forecasting methods of medical items in humanitarian operations. IFAC-PapersOnLine 55 (10), pp.2737-2742. (10.1016/j.ifacol.2022.10.132)
- Petropoulos, F. et al., 2022. Forecasting: theory and practice. International Journal of Forecasting 38 (8), pp.705-871. (10.1016/j.ijforecast.2021.11.001)
- Rostami-Tabar, B. et al. 2022. Forecasting for social good. International Journal of Forecasting 38 (3), pp.1245-1257. (10.1016/j.ijforecast.2021.02.010)
- Rostami-Tabar, B. and Ziel, F. 2022. Anticipating special events in emergency department forecasting. International Journal of Forecasting 38 (3), pp.1197-1213. (10.1016/j.ijforecast.2020.01.001)
- Rostami-Tabar, B. and Boylan, J. 2022. Forecasting and its beneficiaries. In: Salhi, S. and Boylan, J. eds. The Palgrave Handbook of Operations Research. Palgrave Macmillan. , pp.695-717. (10.1007/978-3-030-96935-6_21)
- Rostami-Tabar, B. , Hong, T. and Porter, M. D. 2022. Guest Editorial: Forecasting for social good. International Journal of Forecasting 38 (3), pp.1173-1174.
- Babai, M. Z. , Boylan, J. E. and Rostami-Tabar, B. 2022. Demand forecasting in supply chains: a review of aggregation and hierarchical approaches. International Journal of Production Research 60 (1), pp.324-348. (10.1080/00207543.2021.2005268)
- Mircetic, D. et al., 2022. Forecasting hierarchical time series in supply chains: an empirical investigation. International Journal of Production Research 60 (8), pp.2514-2533. (10.1080/00207543.2021.1896817)
- Wang, Z. et al. 2022. A systematic literature review of trauma networks and systems: from operations management perspectives. Presented at: 12th European Decision Sciences Conference Dublin, Ireland 29 May – 1 June 2022.
2021
- Rostami-Tabar, B. 2021. Business forecasting in developing countries. In: Gilliland, M. , Tashman, L. and Sglavo, U. eds. State of the Art. Wiley and SAS Business Series. , pp.382-387.
- Rostami-Tabar, B. and Rendon-Sanchez, J. F. 2021. Forecasting COVID-19 daily cases using phone call data. Applied Soft Computing 100 106932. (10.1016/j.asoc.2020.106932)
2019
- Rostami-Tabar, B. et al. 2019. The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes. European Journal of Operational Research 273 (3), pp.920-932. (10.1016/j.ejor.2018.09.010)
2017
- Kourentzes, N. , Rostami-Tabar, B. and Barrow, D. K. 2017. Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?. Journal of Business Research 78 (10.1016/j.jbusres.2017.04.016)
- Rostami-Tabar, B. and Disney, S. M. 2017. The bullwhip effect under count time series: The case of first order integer auto-regressive demand processes. Presented at: International Symposium on Industrial Engineering and Operations Management Bristol, UK 25-26 July 2017.
- Rostami-Tabar, B. and Disney, S. M. 2017. The impact of temporal aggregation on production and inventory costs. Presented at: OR59 Annual Conference Loughborough, United Kingdom 12-14 September 2017.
2016
- Emrouznejad, A. , Rostami-Tabar, B. and Petridis, K. 2016. A novel ranking procedure for forecasting approaches using data envelopment analysis. Technological Forecasting and Social Change 111 , pp.235-243. (10.1016/j.techfore.2016.07.004)
2015
- Rostami-Tabar, B. et al. 2015. Non-stationary demand forecasting by cross-sectional aggregation. International Journal of Production Economics 170 (Part A), pp.297-309. (10.1016/j.ijpe.2015.10.001)
2014
- Rostami-Tabar, B. et al. 2014. A note on the forecast performance of temporal aggregation. Naval Research Logistics 61 (7), pp.489-500. (10.1002/nav.21598)
2013
- Rostami-Tabar, B. et al. 2013. Demand forecasting by temporal aggregation. Naval Research Logistics 60 (6), pp.479-498. (10.1002/nav.21546)
2008
- Amin-Naseri, M. and Rostami-Tabar, B. 2008. Neural network approach to lumpy demand forecasting for spare parts in process industries. Presented at: International Conference on Computer and Communication Engineering 13-15 May 2008. 2008 International Conference on Computer and Communication Engineering Proceedings. IEEE. , pp.-. (10.1109/ICCCE.2008.4580831)
- Nasiri Pour, A. , Rostami-Tabar, B. and Rahimzadeh, A. 2008. A hybrid neural network and traditional approach for forecasting lumpy demand. Proceedings of the World Academy of Science, Engineering and Technology 2 (4)
Articles
- Pinson, P. et al., 2026. Editorial: Reproducibility at the International Journal of Forecasting. International Journal of Forecasting (10.1016/j.ijforecast.2026.08.004)
- Halgamuwe Hewage, H. and Rostami-Tabar, B. 2026. A hybrid predictive and prescriptive modelling framework for long-term mental healthcare workforce planning. Journal of the Operational Research Society 77 (9), pp.2529-2553. (10.1080/01605682.2025.2573825)
- Gui, E. M. et al., 2026. What makes the contribution of science towards a sustainable future so difficult? A controversy analysis. Humanities & Social Sciences Communications 13 476. (10.1057/s41599-026-06642-0)
- Birhanu, S. et al., 2026. The role of technology in developing resilient supply chains: A systematic literature review during the COVID-19 pandemic and the disruptions of economic sanctions. Journal of Humanitarian Logistics and Supply Chain Management 16 (1), pp.27-49. (10.1108/JHLSCM-03-2024-0036)
- Wang, Z. et al. 2025. A systematic literature review of trauma systems: An operations management perspective. Advances in Rehabilitation Science and Practice 2024 14. (10.1177/27536351241310645)
- Bilal, A. I. et al., 2025. The missing puzzle piece: contextual insights for enhanced pharmaceutical supply chain forecasting. International Journal of Production Research 63 (24), pp.10189-10207. (10.1080/00207543.2025.2546028)
- Lentlea, D. et al., 2025. Using simulation for long-term bed modelling in critical care. Journal of Simulation 19 (6), pp.657-673. (10.1080/17477778.2024.2412009)
- Omri, O. et al., 2025. On the inventory performance of hierarchical forecasting approaches: case of a vaccine supply chain. Journal of the Operational Research Society (10.1080/01605682.2025.2566323)
- Shi, M. , Rostami-Tabar, B. and Gartner, D. 2025. Looking for the crystal ball in unscheduled care: A systematic literature review of the forecasting process. Health Care Management Science 28 , pp.548-564. (10.1007/s10729-025-09711-z)
- Wang, Z. et al. 2025. Patient length of stay in trauma networks: Insights from advanced modelling. Expert Systems with Applications 289 127801. (10.1016/j.eswa.2025.127801)
- Halagmuwe Hewage, H. et al. 2025. A novel hybrid approach to contraceptive demand forecasting: integrating point predictions with probabilistic distributions. International Journal of Production Research (10.1080/00207543.2025.2526169)
- Rostami-Tabar, B. and Hyndman, R. J. 2025. Hierarchical time series forecasting in emergency medical services. Journal of Service Research 28 (2), pp.278-295. (10.1177/10946705241232169)
- Wang, Z. et al. 2024. Investigating length of stay patterns and its predictors in the South Wales Trauma Network. Advances in Rehabilitation Science and Practice 13 (10.1177/27536351241237866)
- Hasni, M. , Babai, M. Z. and Rostami-Tabar, B. 2024. A hybrid LSTM method for forecasting demands of medical items in humanitarian operations. International Journal of Production Research 62 (17), pp.6046-6063. (10.1080/00207543.2024.2306904)
- Hyndman, R. J. and Rostami-Tabar, B. 2024. Forecasting interrupted time series. Journal of the Operational Research Society (10.1080/01605682.2024.2395315)
- Rostami-Tabar, B. , Browell, J. and Svetunkov, I. 2024. Probabilistic forecasting of hourly Emergency Department arrivals. Health Systems 13 (2), pp.133-149. (10.1080/20476965.2023.2200526)
- Rostami-Tabar, B. , Porter, M. D. and Pinson, P. 2024. Guest editorial: Forecasting for social good. International Journal of Forecasting 41 , pp.1-2. (10.1016/j.ijforecast.2024.08.007)
- Rostami-Tabar, B. et al. 2024. Probabilistic forecasting of daily COVID-19 admissions using machine learning. IMA Journal of Management Mathematics 35 (1), pp.21-43. (10.1093/imaman/dpad009)
- Rostami-Tabar, B. and Gilliland, M. 2024. Alliance or apathy? Forecasting’s role in achieving the U.N. sustainable development goals. Foresight: The International Journal of Applied Forecasting (74), pp.5-6.
- Babaveisi, V. et al., 2023. Integrated demand forecasting and planning model for repairable spare part: an empirical investigation. International Journal of Production Research 61 (20), pp.6791-6807. (10.1080/00207543.2022.2137596)
- Gartner, D. et al. 2023. Challenging the throwaway culture in hospitals: scheduling the mix of reusable and single-use bronchoscopes. Journal of the Operational Research Society 74 (10), pp.2215-2226. (10.1080/01605682.2022.2129490)
- Rostami-Tabar, B. and Mircetic, D. 2023. Exploring the association between time series features and forecasting by temporal aggregation using machine learning. Neurocomputing 548 126376. (10.1016/j.neucom.2023.126376)
- Rostami-Tabar, B. , Babai, Z. and Syntetos, A. 2023. To aggregate or not to aggregate: Forecasting of finite autocorrelated demand. Journal of the Operational Research Society 74 (8), pp.1840-1859. (10.1080/01605682.2022.2118631)
- Abolghasemi, M. , Rostami-Tabar, B. and Syntetos, A. 2023. The value of point of sales information in upstream supply chain forecasting: an empirical investigation. International Journal of Production Research 61 (7), pp.2162-2177. (10.1080/00207543.2022.2063086)
- Rostami-Tabar, B. and Disney, S. 2023. On the order-up-to policy with intermittent integer demand and logically consistent forecasts. International Journal of Production Economics 257 108763. (10.1016/j.ijpe.2022.108763)
- Rostami-Tabar, B. , Goltsos, T. E. and Shixuan, W. 2023. Forecasting for lead-time period by temporal aggregation: Whether to combine and how. Computers in Industry 145 103803. (10.1016/j.compind.2022.103803)
- Rostami-Tabar, B. , Hasni, M. and Babai, Z. 2022. On the inventory performance of demand forecasting methods of medical items in humanitarian operations. IFAC-PapersOnLine 55 (10), pp.2737-2742. (10.1016/j.ifacol.2022.10.132)
- Petropoulos, F. et al., 2022. Forecasting: theory and practice. International Journal of Forecasting 38 (8), pp.705-871. (10.1016/j.ijforecast.2021.11.001)
- Rostami-Tabar, B. et al. 2022. Forecasting for social good. International Journal of Forecasting 38 (3), pp.1245-1257. (10.1016/j.ijforecast.2021.02.010)
- Rostami-Tabar, B. and Ziel, F. 2022. Anticipating special events in emergency department forecasting. International Journal of Forecasting 38 (3), pp.1197-1213. (10.1016/j.ijforecast.2020.01.001)
- Rostami-Tabar, B. , Hong, T. and Porter, M. D. 2022. Guest Editorial: Forecasting for social good. International Journal of Forecasting 38 (3), pp.1173-1174.
- Babai, M. Z. , Boylan, J. E. and Rostami-Tabar, B. 2022. Demand forecasting in supply chains: a review of aggregation and hierarchical approaches. International Journal of Production Research 60 (1), pp.324-348. (10.1080/00207543.2021.2005268)
- Mircetic, D. et al., 2022. Forecasting hierarchical time series in supply chains: an empirical investigation. International Journal of Production Research 60 (8), pp.2514-2533. (10.1080/00207543.2021.1896817)
- Rostami-Tabar, B. and Rendon-Sanchez, J. F. 2021. Forecasting COVID-19 daily cases using phone call data. Applied Soft Computing 100 106932. (10.1016/j.asoc.2020.106932)
- Rostami-Tabar, B. et al. 2019. The impact of temporal aggregation on supply chains with ARMA(1,1) demand processes. European Journal of Operational Research 273 (3), pp.920-932. (10.1016/j.ejor.2018.09.010)
- Kourentzes, N. , Rostami-Tabar, B. and Barrow, D. K. 2017. Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?. Journal of Business Research 78 (10.1016/j.jbusres.2017.04.016)
- Emrouznejad, A. , Rostami-Tabar, B. and Petridis, K. 2016. A novel ranking procedure for forecasting approaches using data envelopment analysis. Technological Forecasting and Social Change 111 , pp.235-243. (10.1016/j.techfore.2016.07.004)
- Rostami-Tabar, B. et al. 2015. Non-stationary demand forecasting by cross-sectional aggregation. International Journal of Production Economics 170 (Part A), pp.297-309. (10.1016/j.ijpe.2015.10.001)
- Rostami-Tabar, B. et al. 2014. A note on the forecast performance of temporal aggregation. Naval Research Logistics 61 (7), pp.489-500. (10.1002/nav.21598)
- Rostami-Tabar, B. et al. 2013. Demand forecasting by temporal aggregation. Naval Research Logistics 60 (6), pp.479-498. (10.1002/nav.21546)
- Nasiri Pour, A. , Rostami-Tabar, B. and Rahimzadeh, A. 2008. A hybrid neural network and traditional approach for forecasting lumpy demand. Proceedings of the World Academy of Science, Engineering and Technology 2 (4)
Book sections
- Rostami-Tabar, B. and Boylan, J. 2022. Forecasting and its beneficiaries. In: Salhi, S. and Boylan, J. eds. The Palgrave Handbook of Operations Research. Palgrave Macmillan. , pp.695-717. (10.1007/978-3-030-96935-6_21)
- Rostami-Tabar, B. 2021. Business forecasting in developing countries. In: Gilliland, M. , Tashman, L. and Sglavo, U. eds. State of the Art. Wiley and SAS Business Series. , pp.382-387.
Books
- Kolassa, S. , Rostami-Tabar, B. and Siemsen, E. 2023. Demand forecasting for executives and professionals. Routledge Taylor & Francis Group. (10.1201/9781003399599)
Conferences
- Wang, Z. et al. 2022. A systematic literature review of trauma networks and systems: from operations management perspectives. Presented at: 12th European Decision Sciences Conference Dublin, Ireland 29 May – 1 June 2022.
- Rostami-Tabar, B. and Disney, S. M. 2017. The bullwhip effect under count time series: The case of first order integer auto-regressive demand processes. Presented at: International Symposium on Industrial Engineering and Operations Management Bristol, UK 25-26 July 2017.
- Rostami-Tabar, B. and Disney, S. M. 2017. The impact of temporal aggregation on production and inventory costs. Presented at: OR59 Annual Conference Loughborough, United Kingdom 12-14 September 2017.
- Amin-Naseri, M. and Rostami-Tabar, B. 2008. Neural network approach to lumpy demand forecasting for spare parts in process industries. Presented at: International Conference on Computer and Communication Engineering 13-15 May 2008. 2008 International Conference on Computer and Communication Engineering Proceedings. IEEE. , pp.-. (10.1109/ICCCE.2008.4580831)
Research
Research
I am interested in developing innovative methodologies in forecasting for social goo and how forecasting, nowcasting and .
Primary research interests
Methodology:
- Uncertainty and the Future
- Modelling and Analytics
- Data Science, Machine learning & AI
- Probabilsitic Forecasting
- Tools for thinking about the future
- Operational Reserach
Applications:
- Social Good
- Health and Care Systems
- Global and Public Health Supply Chains
- Humanitarian Supply Chains
Teaching
Teaching commitments
- Forecasting
- Business Data Analytics
- Healthcare Planning Evidence & Analytics
- Inventory Management
- Supply Chain Planning
I have been delivering trainings on Data Analytics and Forecasting using R since 2018. I have trained over 500 people in the UK (mainly NHS analysts), Tunisia, Senegal, Nigeria, Uganda, Iraq, Turkey, Indonesia and Georgia so far. My trainings cover various topics including how to prepare, manipulate and visualize data, apply various forecasting models and evaluate their forecast accuacy in R.
If you need a training in these areas, get in touch.
Biography
Qualifications
- 2014 - Ph.D., Industrial Engineering. Thesis entitled 'ARIMA Demand Forecasting by Aggregation', University of Bordeaux, France
- 2010 - M.Sc., Information Systems, ECE Paris, France
- 2009 - Proficiency in French Language, Université de Poitiers , France
- 2008 - M.Sc., Industrial Engineering, Tarbiat Modares University, Tehran, Iran
- 2004 - B.Sc., Industrial Engineering, K.N.Toosi Uni. Of Technology, Tehran, Iran (4 years).
Honours and awards
- 2024, Goodeve Medal winner, Operational Research Society, UK.
- 2024, Fellowship, Institute of Advanced Studies, Montpellier, France.
- 2021, Public Value Fellow, Cardiff Business School.
- 2021, Associate Fellow, NHS-R community.
- 2013 MIM best paper award (IFAC).
- 2017 International Symposium on Industrial Engineering and Operations Management, Best Track Paper Award, Bristol, UK.
Academic positions
- 2015 - 2016: Lecturer in Supply Chain Management, School of Strategy and Leadership, Coventry University, UK.
- 2014 - 2015: Postdoctoral Research Fellowship, Industrial Engineering Lab, Ecole Centrale Paris, France.
Supervisions
PhD Supervision
I welcome enquiries from potential PhD students in the areas of
Theory & methodology:
- Uncertainty and the future
- Artificial Intelligence
- Machine Learning
- Probabilistic Forecasting
- Forecasting by Aggregation (Hierarchical and Temporal)
- Operational Research
Applications:
- Healthcare Systems
- Global and Public Health Supply Chains
- Humanitarian Logistics & Supply Chains
- Social Good
- UN Sustainable Development Goals
Current supervision
Alfred Sallwa
Mingzhe Shi
Harsha Ruwan Chamara Halgamuwe Hewage
PhD Student/Research Assistant
Yemsrach Demissie
Birhanu Shanko Dura Dura
Past projects
1. Dr. Diego Bermudez Bermejo, 2020-2024, An integrative multifactor systems approach to enhance the operational resilience and sustainability of the UK retail payments’ organisational stakeholders, currently working at Ellen MacArthur Foundation
News articles
Contact Details
[email protected]
+44 29208 70723
Aberconway Building, Room C08, Colum Road, Cathays, Cardiff, CF10 3EU
+44 29208 70723
Aberconway Building, Room C08, Colum Road, Cathays, Cardiff, CF10 3EU
Research themes
Specialisms
- Artificial intelligence
- Machine learning
- Forecasting
- Healthcare
- Supply chains
