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Personal profile


Dr Alireza Daneshkhah holds a PhD (Warwick), is a fellow of the Royal Statistical Society and a member of International Society of Bayesian Analysis. He is a Senior Lecturer in Statistics and Course Director of Data Science and Computational Intelligence (MSc) in the School of Engineering, Environment and Computing of Coventry University. Alireza is Bayesian statistician interested in applying Bayesian probabilistic methods in a wide range of applications. These include modelling interdependencies of large scale data and simulation of complex systems using Bayesian networks, Gaussian process emulators and artificial neural networks. In the recent years, his research has been involved in developing Proactive asset management using optimised time-based maintenance and online condition based maintenance for networked infrastructure using advanced dynamic graphical models in the presence of massive heterogeneous information, including on-line data (SCADA and sensor data). He is also interested in probabilistic risk analysis of climate change affecting water systems and reliability analysis of the assets under natural hazard threats. His other research interests are using expert elicitation techniques when the available data is limited, and modelling Big data using a wide range of Machine learning techniques. Ali’s most recent research interest is to develop Deep learning methods using Deep Gaussian process and neural networks with a wide range of applications including uncertainty quantification of highly complex PDE based models, autonomous vehicle validation and testing, processing medical images, remote sensing, etc.

PhD Project

I teach various undergraduate and postgraduate modules on statistics, Artificial intelligence and Machine Learning and supervise PhD students both within CEM and jointly with other centres. My main focus is on topics in  Deep laerning using Deep Gaussain process, Probabilstic Uncertainty quantification and Sensitivity analysis, eliciting probability distributions, and modelling Big Data using Graphical models (particularly, Bayesian networks) and multivariate copulas (known as pair-copula vine).


Education/Academic qualification

Bayesian Statistics, Doctorate, University of Warwick

1 Oct 199924 Jun 2004

Statistics, Postgraduate Certificate, Shahid Beheshti University

30 Sep 199431 Aug 1996

Statistics, Degree, Shahid Chamran University of Ahvaz

16 Sep 199010 Aug 1994


  • QA75 Electronic computers. Computer science
  • Probabilistic Bayesian modelling
  • Gaussian Process
  • Bayesian networks
  • Expert elicitation
  • Sensitivity analysis
  • Uncertainty quantification
  • Reliability analysis
  • risk assessment
  • Preventive maintenance
  • Big data

Fingerprint Dive into the research topics where Alireza Daneshkhah is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Copula Mathematics
Bayesian networks Engineering & Materials Science
Bayesian Networks Mathematics
Sensitivity analysis Engineering & Materials Science
Probability distributions Engineering & Materials Science
Basis Functions Mathematics
Preventive maintenance Engineering & Materials Science
Costs Engineering & Materials Science

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Research Output 2006 2019

Optimizing minimum information pair-copula using genetic algorithm to select optimal basis functions

Chatrabgoun, O., Daneshkhah, A. & Esmaeilbeigi, M., 7 Feb 2019, In : Communications in Statistics - Simulation and Computation. 48, 2, p. 494-505 12 p.

Research output: Contribution to journalArticle

Basis Functions
Genetic algorithms
Genetic Algorithm
2 Downloads (Pure)

Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification

Batsch, F., Daneshkhah, A., Cheah, M., Kanarachos, S. & Baxendale, A., 2019, 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. IEEE, p. 419-424 6 p. 8917119

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Open Access
3 Citations (Scopus)
7 Downloads (Pure)

Approximating non-Gaussian Bayesian networks using minimum information vine model with applications in financial modelling

Chatrabgoun, O., Hosseinian-Far, A., Chang, V., Stocks, N. & Daneshkhah, A., Jan 2018, In : Journal of Computational Science. 24, p. 266-276 11 p.

Research output: Contribution to journalArticle

Open Access
Financial Modeling
Bayesian networks
Bayesian Networks
Heavy Tails

Crime Data Mining, Threat Analysis and Prediction

Farsi, M., Daneshkhah, A., Hosseinian-Far, A. & Chatrabgoun, O., 2018, Cyber Criminology. Jahankhani, H. (ed.). Springer, p. 183-202 20 p.

Research output: Chapter in Book/Report/Conference proceedingChapter

law enforcement

Discrete Weighted Exponential Distribution of the Second Type: Properties and Applications

Rasekhi, M., Chatrabgoun, O. & Daneshkhah, A., 2018, In : FILOMAT. 32, 8, p. 3043–3056 14 p., 6262.

Research output: Contribution to journalArticle

Open Access
Weighted Distributions
Exponential distribution
Hazard Rate Function
Reliability Index
Probability generating function