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Biography
Dr Alireza Daneshkhah is an Associate Professor, and Curriculum Lead Data Science and AI. He is an associate of Coventry research centre for Computational Science and Mathematical Modelling (CSMM). Dr Daneshkhah received his PhD degree from the University of Warwick for a thesis titled "Estimation in causal graphical models". Prior to his current position, he was a member of the Warwick Centre for Predictive Modelling where he has developed deep learning methods to probabilistically simulate highly complex systems. He also served a course director of Utility asset management at Water Institute of Cranfield University. His primary research is in Bayesian elicitation of expert’s probabilistic statements and model structure; modelling high-dimensional data using Bayesian networks, Dynamic Bayesian networks, and Pair-copula Bayesian network models; and simulating highly complex Engineering and Environmental systems using Gaussian process emulators and Deep learning approaches. He has applied these methods to a wide range of applications including urban and coastal flood modelling, health, economics, decision-making under uncertainty and risk assessment of networked systems.
He has served as principal investigator, collaborator and researcher to several EPSRC, NHS, NERC, DEFRA, and industrial-based research projects in developing various Bayesian Machine Learning methods in tackling highly complex engineering and environmental case studies in the presence of both limited and Big Data.
Dr Alireza Daneshkhah is co-author of three books in expert judgment, advanced reliability methods, and digital twins and has an established list of published journal papers, book chapters and conference communications to his name. Dr Alireza Daneshkhah is a fellow of the Royal Statistical Society, a member of International Society of Bayesian Analysis, and an associate of Institute of Mathematics and its Applications.
Research Interests
- Bayesian Statistics
- Simulating/approximating complex systems using Gaussian processes
- Modelling big data using Bayesian network (BN), Dynamic BN, and multivariate copula models.
- Probabilistic sensitivity analysis and Bayesian uncertainty quantification
- Deep learning using Deep Gaussian process, and deep AI methods;
- Risk assessment and Reliability analysis of complex industrial and environmental systems;
- Elicitation of expert knowledge and opinions;
- Modelling and forecasting extreme climatic events using Machine Learning and AI methods.
- Simulation Methodologies for Autonomous Vehicle
Teaching Modules
- 321MP - Bayesian Statistics
- 7088CEM - Artificial Neutral Networks
- 7135CEM - Modelling and Optimisation Under Uncertainty
PhD Project
I am currently supervising several PhD students both within the Faculty centre of CSMM and jointly with other research centres. My main focus is on topics in Deep laerning using Deep Gaussain process, Probabilstic Uncertainty quantification and Sensitivity analysis, and modelling Big Data using Graphical models (particularly, Bayesian networks) and multivariate copulas (known as pair-copula vine) with wide range of applications, including modelling disease data, topic modelling for precision medicine, abetes type I data with various complications, modelling and forecasting extreme climatic events, catastrohe modelling, simulation methodologies for Autonomous Vehicle, etc.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Bayesian Statistics, Doctorate, Estimation in Causal Graphical Models, University of Warwick
1 Oct 1999 → 24 Jun 2004
Award Date: 24 Jun 2004
Statistics, Postgraduate Certificate, Bayesian Robustness in Finite Population Sampling, Shahid Beheshti University
30 Sept 1994 → 31 Aug 1996
Award Date: 7 Aug 1996
Statistics, Degree, Shahid Chamran University of Ahvaz
16 Sept 1990 → 10 Aug 1994
Award Date: 10 Aug 1994
Keywords
- QA75 Electronic computers. Computer science
- Probabilistic Bayesian modelling
- Gaussian Process
- Bayesian networks
- Expert elicitation
- Sensitivity analysis
- Uncertainty quantification
- Reliability analysis
- risk assessment
- Preventive maintenance
- flood modelling
- Climate Change
- Hydrodynamic modelling
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Collaborations and top research areas from the last five years
Projects
- 1 Finished
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Supporting Delivery of the Coventry Household Survey
1/01/20 → 31/03/20
Project: Internally funded project
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A novel algorithm for classification using a low rank approximation of kernel-based support vector machines with applications
Chatrabgoun, O., Esmaeilbeigi, M., Daneshkhah, A., Kamandi, A. & Salimi, N., 28 Jul 2023, (E-pub ahead of print) In: Communications in Statistics - Simulation and Computation. (In-Press), p. (In-Press) 21 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile4 Downloads (Pure) -
Artificial Intelligence for skeleton-based physical rehabilitation action evaluation: A systematic review
Sardari, S., Sharifzadeh, S., Daneshkhah, A., Nakisa, B., Loke, S. W., Palade, V. & Duncan, M., May 2023, In: Computers in Biology and Medicine. 158, 21 p., 106835.Research output: Contribution to journal › Review article › peer-review
Open AccessFile30 Downloads (Pure) -
Challenges and prospects of climate change impact assessment on mangrove environments through mathematical models
Fanous, M., Eden, J., Remesan, R. & Daneshkhah, A., Apr 2023, In: Environmental Modelling & Software. 162, 14 p., 105658.Research output: Contribution to journal › Review article › peer-review
Open AccessFile17 Downloads (Pure) -
Hydro-morphodynamic modelling of mangroves imposed by tidal waves using finite element discontinuous Galerkin method
Fanous, M., Daneshkhah, A., Eden, J. M., Remesan, R. & Palade, V., 28 Mar 2023, In: Coastal Engineering. 182, 19 p., 104303.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Citation (Scopus)20 Downloads (Pure) -
Measuring local sensitivity in Bayesian inference using a new class of metrics
Sedighi, T., Hosseinian-Far, A. & Daneshkhah, A., 2023, In: Communications in Statistics - Theory and Methods. 52, 11, p. 3581-3597 17 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile1 Citation (Scopus)20 Downloads (Pure)