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Dr. Alireza Daneshkhah

Dr Alireza Daneshkhah

Dr Alireza Daneshkhah

Associate Professor in AI, Faculty of Mathematics and Data Science

Telephone: +971 4 6050181


Dr. 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, Dr. 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.

  • 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.
  • Simulating complex Mathematical models/System using:
    • Gaussian Process Emulators for Uncertainty Quantification & Sensitivity Analysis.
    • Physics-informed Neural Networks for PDEs and dynamic systems.
  • 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.
  • Bayesian elicitation of expert opinions applied in various health and medical sciences.
  • 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 – 2024
    • Donnelly, J, Daneshkhah, A., and Abolfathi, S. (2024). Physics-Informed Neural Networks as Surrogate Models of Hydrodynamic Simulators. Science of The Total Environment, 912 Link
    • 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 Link
    • 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 Link
    • 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 Link.
    • 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 Link.
    • 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 Link
    • 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 Link
    • 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 Link
    • 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 Link
    • 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 Link.
    • 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 Link
    • 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 Link.
    • 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 Link.
    • 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 Link.  
    • 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 Link
    • 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 Link
    • 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 Link
    • 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 Link
    • 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 Link
    • 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 Link