Prof. Alireza Daneshkhah

Professor in Data Science, Faculty of Mathematics and Data Science

Biography

Prof. Alireza Daneshkhah earned his PhD in 'Estimation in Causal Graphical Models' from the University of Warwick, UK, and a PgCert in Higher Education from Coventry University. Before his current position at the EAU, he held the position of Associate Professor and Curriculum Lead in Data Science and AI at Coventry University. He was also associated with the Coventry Research Centre for Computational Science and Mathematical Modelling. Previously, Prof. Daneshkhah was a member of the Warwick Centre for Predictive Modelling, focusing on developing deep learning methods for probabilistic simulations of complex real-world systems. Additionally, he directed MSc Utility Asset Management at the Water Institute of Cranfield University. His research is centred on Bayesian elicitation of expert opinions, high-dimensional data modelling using various Graphical Models, and simulating complex Engineering and Environmental systems with Gaussian process emulators and Physics-informed Machine Learning models. These methods were applied to diverse real-world challenges like urban/coastal flood modelling, health, economics, and decision-making under uncertainty. He has been involved in numerous research projects funded by EPSRC, NHS, NERC, DEFRA, and industry partners, focusing on developing AI and Bayesian Machine Learning methods for tackling diverse applications in climate change, digital health, and asset management of networked infrastructure with limited/Big Data.

alireza.daneshkhah@eau.ac.ae
+971 4 6050181
https://ek-au-qa-umbraco.azurewebsites.net/faculty-member/prof-alireza-daneshkhah
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Education

PgCert in Academic Practice in Higher Education, Coventry University, UK 2018.
PhD in Bayesian Statistics, The University of Warwick, UK, 2004.
MSc in Statistics, Shahid Beheshti University of Tehran, Iran, 1996.
BSc in Statistics, Shahid Chamran University of Ahvaz, Iran, 1994.

Research Area | Simulating complex Mathematical models/System using:

Gaussian Process Emulators for Uncertainty Quantification & Sensitivity Analysis.
Physics-informed Neural Networks for PDEs and dynamic systems.

Research Area | Modelling high-dimensional data with:

Probabilistic models like Bayesian networks and Pair-copula Vine models.
Deep Learning techniques such as CNN, TCN, GAN, Transformers for skeleton/pose data.

Research Area | Bayesian elicitation of expert opinions applied in various health and medical sciences.

Publications

Books:

O' Hagan, A., Buck, C. E., \textbf{Daneshkhah, A.}, Eiser, J. E., Garthwaite, P. H., Jenkinson, D. J., Oakley, J. E. and Rakow, T. (2006). Uncertain Judgements - Eliciting Expert Probabilities. John Wiley and Sons.
Bedford, T., Walls, L., Quigley, J., Alkali, B., Daneshkhah, A. and Hardman, G. (2008). Advances in Mathematical modelling for Reliability. IOS Press, Amsterdam.
Farsi, M., Daneshkhah, A., Hosseinian-Far, A., and Jahankhani, H. (Eds.). (2020). Digital Twin Technologies and Smart Cities. Springer International Publishing.

Selected Recent Publications 2021 – 2025

Salari, N., Heidarian, P., Tarrahi, M.J., Mansourian, M., Canbary, Z., Daneshkhah, A., Nasirian, M., Faghihi, S.H., Mohammadi, M. Global prevalence of eating disorders in children: a comprehensive systematic review and meta-analysis (2025) Italian Journal of Pediatrics, 51 (1).
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Salari, N., Beiromvand, M., Abdollahi, R., Hemmatabadi, F.K., Daneshkhah, A., Ghaderi, A., Asgari,M., Mohammadi, M.Global prevalence of hydrocele in infants and children: a systematic review and meta-analysis (2025) BMC Pediatrics, 25 (1), art. no. 128.
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Salari, N., Razavizadeh, S., Abdolmaleki, A., Zarei, H., Daneshkhah, A., Mohammadi, M.Global prevalence of loneliness in immigrants: A systematic review and meta-analysis(2025) Asian Journal of Psychiatry, 104.
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Chatrabgoun, O., Daneshkhah, A., Torkaman, P., Johnston, M., Sohrabi Safa, N., Kashif Bashir, A.Covariate-adjusted construction of gene regulatory networks using a combination of generalized linear model and penalized maximum likelihood (2025) PLoS ONE, 20 (1 January).
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Sardari, S., Sharifzadeh, S., Daneshkhah, A., Loke, S.W., Palade, V., Duncan, M.J., Nakisa, B. LightPRA: A Lightweight Temporal Convolutional Network for Automatic Physical Rehabilitation Exercise Assessment (2024) Computers in Biology and Medicine, 173.
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Donnelly, J, Daneshkhah, A., and Abolfathi, S. (2024). Physics-Informed Neural Networks as Surrogate Models of Hydrodynamic Simulators. Science of The Total Environment, 912.
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Donnelly, J, Daneshkhah, A., and Abolfathi, S. (2024). Forecasting global climate drivers using Gaussian processes and convolutional autoencoders. Engineering Applications of Artificial Intelligence, 128, 107536.
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Shrinivas, V., Bastien, C., Davies, H., Daneshkhah, A., Hardwicke, J., Neal-Sturgess, C., and Lamaj, A. (2024). Integrating Machine Learning in Pedestrian Forensics: A Comprehensive Tool for Analysing Pedestrian Collisions. No. 2024-01-2468, SAE Technical Paper.
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Sardari, S., Sharifzadeh, S., Daneshkhah, A., Loke, S. W., Palade, V., Duncan, M. J., and Nakisa, B. (2024). LightPRA: A lightweight Temporal Convolutional Network for automatic physical rehabilitation exercise assessment. Computers in Biology and Medicine, 108382
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Fanous, M., Daneshkhah, A. Eden, J. M., Remesan, R., Palade, V. (2023). Hydro-morphodynamic Modelling of Mangroves Imposed by Tidal Waves Using Finite Element Discontinuous Galerkin Method. Coastal Engineering, 104303
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Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S. W., Palade, V., and Duncan, M. J. (2023). Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review. Computers in Biology and Medicine, 106835
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Esmaeilbeigi, M., Chatrabgoun, O., Daneshkhah, A., & Shafa, M. (2023). On the impact of prior distributions on efficiency of sparse Gaussian process regression. Engineering with Computers, 39(4), 2905-2925
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Al Ali, H., Daneshkhah, A., Boutayeb, A., and Mukandavire, Z. (2022). Examining Type 1 Diabetes Mathematical Models Using Experimental Data. International Journal of Environmental Research and Public Health, 19(2), 737
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Al Ali, H., Daneshkhah, A., Boutayeb, A., Malunguza, N. J., and Mukandavire, Z. (2022). Exploring dynamical properties of a Type 1 diabetes model using sensitivity approaches. Mathematics and Computers in Simulation, 201 (November), 324-342
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Salari, N., Hosseinian-Far, A., Mohammadi, M., Ghasemi, H., Khazaie, H., Daneshkhah, A., and Ahmadi, A. (2022). Detection of sleep apnea using Machine learning algorithms based on ECG Signals: A comprehensive systematic review. Expert Systems with Applications, 187, 115950
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Ni Ki, C., Hosseinian‐Far, A., Daneshkhah, A., Salari, N. (2021). Topic modelling in precision medicine with its applications in personalized diabetes management. Expert Systems, e12774
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Andayeshgar, B., Abdali-Mohammadi, F., Sepahvand, M., Daneshkhah, A., Almasi, A., \& Salari, N. (2022). Developing Graph Convolutional Networks and Mutual Information for Arrhythmic Diagnosis Based on Multichannel ECG Signals. International Journal of Environmental Research and Public Health, 19(17), 10707
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Donnelly, J., Abolfathi, S., Pearson, J., Chatrabgoun, O., and Daneshkhah, A. (2022). Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model. Water Research, 225, 119100.
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Shrinivas, V., Bastien, C., Davies, H., Daneshkhah, A., and Hardwicke, J. (2022). Parameters influencing pedestrian injury and severity–A systematic review and meta-analysis. Transportation Engineering, 100158.
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Spooner, J., Palade, V., Cheah, M., Kanarachos, S., and Daneshkhah, A. (2021). Generation of Pedestrian Crossing Scenarios Using Ped-Cross Generative Adversarial Network. Applied Sciences, 11(2), 47.
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Batsch, F., Daneshkhah, A., Palade, V., and Cheah, M. (2021). Scenario Optimisation and Sensitivity Analysis for Safe Automated Driving Using Gaussian Processes. Applied Sciences, 11(2), 775.
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Rakhshan, K., Morel, J. C., and Daneshkhah, A. (2021). A probabilistic predictive model for assessing the economic reusability of load-bearing building components: Developing a Circular Economy framework. Sustainable Production and Consumption, 27, 630-642.
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Rakhshan, K., Morel, J. C., and Daneshkhah, A. (2021). Predicting the technical reusability of load-bearing building components: A probabilistic approach towards developing a Circular Economy framework. Journal of Building Engineering, 102791.
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Vepa, A., Saleem, A., Rakhshan, K., Daneshkhah, A.*, et al. (2021). Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients. International Journal of Environmental Research and Public Health, 18(12), 6228.
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Sedighi, T., Varga, L., Hosseinian-Far, A., and Daneshkhah, A. (2021). Economic evaluation of mental health effects of flooding using Bayesian networks. International journal of environmental research and public health, 7467.
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