Contact Dr Tabassom Sedighi
Areas of expertise
- Applied Informatics
- Computational Fluid Dynamics
- Computing, Simulation & Modelling
- Environment and Health
- Safety, Resilience, Risk & Reliability
- Sustainable Manufacturing
- Systems Engineering
- Throughlife Engineering Services
Tabassom Sedighi gained her BSc in Physics and undertook her MSc in Control Engineering (Coventry University, UK). Her primary research interests span in Physics, Control Engineering, mathematical modelling, adaptive controller design, nonlinear observer design, fault detection and isolation, error stability analysis, solving differential equations and Lyapunov equations, data analysis, risk analysis and predicting residual useful life. She had her PhD under the supervision of Prof. Peter Foote and Prof. Andrew Starr at Cranfield University as part of the No Fault Found (NFF) project sponsored by EPSRC and BAE systems. She has been actively involved in the NFF project regarding intermittent fault detection and prediction of test facilities for assessment of intermittent fault patterns and deployment statistical methods (Dynamic Bayesian Network, Gaussian process, etc.) for intermittent fault prediction.
After her PhD she had the responsibility as a research fellow for interpreting case studies, collected data, practitioner practices and policies for governance, into a Systems Dynamics (SD) model capable of handling the requisite variety of cases and developing insights into the evaluation of different policies. She was also working with the CECAN team to understand how this SD tool fits into the landscape of methods for complexity evaluation. She was disseminating findings and helping to develop capacity in policymakers and related stakeholders by supporting their use of the SD tool.
I am interested in the following areas:
Statistics and Machine Learning techniques ( Bayesian methods), working with large volume of data, modelling and simulation complex systems using machine learning techniques with applications in complex engineering, social, industrial and environmental systems; infectious disease modelling; decision making and risk analysis; prediction; pattern recognition; fault and degradation detection.
Modelling inter-dependencies between system components using Bayesian models with application in optimised decision making, and system reliability evaluation; system forecasting; uncertainty Quantification/propagation using Gaussian Process emulators; Optimized decision making under uncertainty and the Game theory.
Control theory and its applications.
I am an internationally recognised control engineer and Bayesian modeller. Focus on different aspects of machine learning and statistical techniques to predict national soil properties. Developing Bayesian modelling through postgraduate, doctoral and researching. Member of United Kingdom Automatic Control Council (UKACC), Member of International Federation of Automatic Control (IFAC), Member of Complex Systems Society (CSS), Associate Editor for International Journal of Strategic Engineering (IJoSE).
BAE Systems; DEFRA; Environment Agency.
Articles In Journals
- Sedighi T & Varga L (2021) Evaluating the bovine tuberculosis eradication mechanism and its risk factors in England’s cattle farms, International Journal of Environmental Research and Public Health, 18 (7, April) Article No. 3451.
- Naghshbandi SN, Varga L, Purvis A, Mcwilliam R, Minisci E, Vasile M, Troffaes M, Sedighi T, Guo W, Manley E & Jones DH (2020) A review of methods to study resilience of complex engineering and engineered systems, IEEE Access, 8 (May) 87775-87799.
- Daneshkhah A, Hosseinian-Far A, Chatrabgoun O, Sedighi T & Farsi M (2018) Probabilistic modeling of financial uncertainties, International Journal of Organizational and Collective Intelligence, 8 (2) Article No. 1.
- Sedighi T, Foote PD & Sydor P (2017) Feed-forward observer-based intermittent fault detection, CIRP Journal of Manufacturing Science and Technology, 17 (May) 10-17.
- T Sedighi, PD Foote & S Khan (2014) The Performance of Observer-based Residuals for Detecting Intermittent Faults: The Limitations, Procedia CIRP, 22 65-70.
- Tabassom Sedighi & Liz Varga (2018) Using Dynamic Bayesian Network for Decision-Making and Evaluation of Polices in Nexus despite their Incredible Complexity. In: Conference on Complex Systems, Thessaloniki, 23-28 September 2018.
- Sedighi T, Khan S & Foote P. (2018) Intermittent fault detection on an experimental aircraft fuel rig: Reduce the No Fault Found rate. In: 2015 4th International Conference on Systems and Control (ICSC), Sousse, 28-30 April 2015.
- Sedighi T, Phillips P & Foote P. (2013) Model-based intermittent fault detection. In: 2nd International Through-life Engineering Services Conference, Durham University, 5-6 November 2013.
- Sedighi T, Koshkouei AJ & Burnham K. (2010) Nonlinear unknown input observer design for nonlinear systems. In: UKACC International Conference on Control 2010, Coventry, 7-10 September 2010.
- Sedighi T, Koshkouei AJ & Burnham KJ. (2008) Observer-Based Residual Design for Nonlinear Systems. In: UKACC Control Conference, University of Manchester, 2-4 September 2008.
- Sedighi T & Koshkouei AJ. (2007) Optimal-Backstepping Approach for Fault Detection of Nonlinear Systems. In: Intelligent Systems and Control (ISC 2007), Cambridge, MA, 19-21 November 2007.
- Sedighi T, Koshkouei AJ & Burnham K. (2006) Fault detection for massspring-damper systems: backstepping approach. In: 18th International Conference on Systems Engineering (ICSE–06),, Control Theory & Applications Centre, Coventry University, 8-10 September 2006.
- Farsi M, Hosseinian-Far A, Daneshkhah A & Sedighi T (2017) Mathematical and computational modelling frameworks for integrated sustainability assessment (ISA). In: Strategic Engineering for Cloud Computing and Big Data Analytics, Cham: Springer International Publishing, p. 3-27. Dataset/s: 10.1007/978-3-319-52491-7_1
- Daneshkhah A, Hosseinian-Far A, Sedighi T & Farsi M (2017) Prior elicitation and evaluation of imprecise judgements for Bayesian analysis of system reliability. In: Strategic Engineering for Cloud Computing and Big Data Analytics. Hosseinian-Far A, Ramachandran M, Sarwar D (ed.), Springer International Publishing, p. 63-79. Dataset/s: 10.1007/978-3-319-52491-7_4