Professor Edoardo Patelli
Civil and Environmental Engineering
Personal statement
Personal statement
I am a professor in Risk and Uncertainty Quantification and the Head of the Centre for Intelligent Infrastructure at the Department of Civil and Environmental Engineering. I am working in a multidisciplinary environment in collaboration with members from all the university faculties and strong links with world-leading scholars. I am the Chair of the Technical Committee on Simulation for Safety and Reliability Analysis of the European Safety and Reliability Association (ESRA), Chair of the Technical Committee for the 2019 and 2022 European Safety and Reliability Conference (ESREL), Member of the Committee on Probability and Statistics in the Physical Sciences (part of the Bernoulli Society), Academic Adviser to the Commonwealth Scholarship Commission and member of the UK Nuclear Innovation and Research Advisory Board (NIRAB). My current research interests are in:
- Nuclear safety
- Efficient and readable numerical methods for uncertainty quantification
- Resilience Engineering for critical infrastructure
- Digital twin and efficient simulation methods capable of managing and quantifying uncertainty
- Developing Trustful AI and Learning Algorithms for Imprecise and Bad Data
- On-line monitoring tools for nuclear systems, Civil infrastructure and aerospace
- Human reliability analysis and interaction with autonomous systems
- Risk communication
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Prize And Awards
- The NASA and DNV Challenge on Optimization under Uncertainty
- Recipient
- 17/6/2025
- Best Paper Award in "Innovation in Smart Cities and Systems"
- Recipient
- 1/9/2022
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Publications
- Modeling market adaptation and regulatory flexibility for fuel supply resilience
- Farias Duque Jardel, Moura Raphael, Patelli Edoardo
- Proceedings of the European Safety and Reliability Conference (ESREL2026) European Safety and Reliability Conference, pp. 2141-2147 (2026)
- https://doi.org/10.3850/ESREL2026061419_esrel26-p28956-cd
- Risk-informed integration of renewable energy systems and storage in electric power grids : Assessing safety and economic viability via an efficient approach
- Cangul Ozcel, Rocchetta Roberto, Patelli Edoardo
- Energy Reports Vol 15 (2026)
- https://doi.org/10.1016/j.egyr.2026.109338
- Damage diagnosis of bridge structures using deep learning strategies : a hybrid neural networks practical tool
- Xiang ChangSheng, Zhao Hua, Wu GuoJi, Chen LiJuan, Yang Zhen, Patelli Edoardo
- Structural Health Monitoring, pp. 1-28 (2026)
- https://doi.org/10.1177/14759217261445344
- Enhancing procedure quality : advanced language tools for identifying ambiguity and high-potential violation triggers
- Johnson Karl, Morais Caroline, Patelli Edoardo
- Reliability Engineering and System Safety Vol 264 (2025)
- https://doi.org/10.1016/j.ress.2025.111308
- On the computational complexity of the interval dependence problem in credal networks
- de Angelis Marco, Estrada-Lugo Hector Diego, Ferson Scott, Patelli Edoardo
- Digitalisation and Digital Transformation Communications in Computer and Information Science, pp. 113–118 (2025)
- https://doi.org/10.1007/978-3-032-04731-1_15
- Robust storm surge forecasts for early warning system : a machine learning approach using Monte Carlo Bayesian model selection algorithm
- MacDonald E, Tubaldi E, Patelli E
- Stochastic Environmental Research and Risk Assessment Vol 39, pp. 2789-2816 (2025)
- https://doi.org/10.1007/s00477-025-02993-3
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Teaching
I am currently teaching:
- Probability and statistics
- Structural reliability
- Monte Carlo methods
- Bayesian approaches
- FMEA, Fault Tree, Event Tree
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Research Interests
My primary research interests focus on the general area of risk analysis, uncertainty modelling and quantification, sensitivity analysis, nuclear safety, reliability and availability of complex systems. In particular, the focus is on the development of verified and efficient stochastic computational methods able to model different representation of the uncertainty and providing trustful reliability analysis and risk assessment.
The numerical implementations have resulted in the development of an open-source general-purpose software package for uncertainty quantification and stochastic analysis.
Professional Activities
- Strategic Themes - EPSRC (Critical Mass) - IES Workshop
- Participant
- 7/2/2025
- EPSRC Scoping Worskhop (Critical mass)
- Participant
- 30/1/2025
- Frontiers Nuclear Engineering (Journal)
- Peer reviewer
- 1/8/2024
- Changsheng Xiang
- Host
- 1/1/2024
- Frontiers Nuclear Engineering (Journal)
- Associate Editor
- 30/6/2023
- 5th International Conference on Uncertainty Quantification in Computational Science and Engineering
- Keynote/plenary speaker
- 14/6/2023
Projects
- COST Action: CA25138 - Secure and Adaptive Frameworks for Environmentally sustainable Offshore WIND expansion (SAFEWIND)
- Dethlef, Nina (Principal Investigator) Kamranzad, Bahareh (Co-investigator) Patelli, Edoardo (Co-investigator) Carroll, James (Co-investigator)
- Europe requires a seven-fold expansion of offshore wind (OSW) capacity by 2030 to achieve its ambitious net zero goals. Meeting this target in a way that is safe, resilient and sustainable requires new turbine structures but also solutions to bottlenecks in grid and port infrastructure, streamlined regulatory processes and optimisation of socio-economic benefits for coastal communities. Co-existence adds further complexity through OSW interactions with fisheries, sea users and marine ecosystems, where long-term impacts remain poorly understood. Artificial Intelligence (AI) has potential to enable transformative change to OSW site planning and logistics, impact assessment and workforce training. It can inform processes to overcome barriers across regulation, infrastructure, and coexistence. However, with growing automation of operational infrastructure, new vulnerabilities arise. Cybersecurity risks, such as external model manipulation, data theft, and unsafe agent behaviour, pose threats to turbines, grids, and autonomous vessels, and could undermining energy security. AI’s own environmental footprint has also come under scrutiny, highlighting the need for low-cost, energy-efficient algorithms. Moreover, climate change introduces deep uncertainty for OSW design and operations through shifts in wind patterns, rising sea levels, and intensifying storms that can complicate energy yield forecasts, structural health monitoring, and vessel access. AI can support adaptation by downscaling climate models, forecasting extreme events, and embedding risk-aware learning into digital twins. This COST Action convenes a balanced, inclusive, geographically-diverse network of experts. By highlighting gaps in data, knowledge, regulation, and best practice, it will create roadmaps towards safe, sustainable, digitally-enabled OSW growth and strengthen Europe’s OSW leadership.
Funding: €140K per year (duration: 4 years) - 19-Jan-2026 - 18-Jan-2030
- Feasibility of Small Modular Reactors for Zero-Emission Ships & Ports (NZCLEAR)
- Theotokatos, Gerasimos (Principal Investigator) Boulougouris, Evangelos (Co-investigator) Patelli, Edoardo (Co-investigator) Ward, Michael (Co-investigator)
- 01-Jan-2026 - 31-Jan-2026
- Feasibility of Small Modular Reactors for Zero-Emission Ships & Ports
- Karvounis, Panagiotis (Principal Investigator) Theotokatos, Gerasimos (Co-investigator) Boulougouris, Evangelos (Co-investigator) Patelli, Edoardo (Co-investigator) Ward, Michael (Co-investigator)
- £39,964.61
- 01-Jan-2026 - 31-Jan-2027
- Real-time uncertainty quantification in a Digital Twin for Emergency Response to Scottish Ambulance Service(DRNet+ funding call)
- Patelli, Edoardo (Principal Investigator) Fossati, Marco (Co-investigator) Basu, Tathagata (Research Co-investigator)
- 01-Jan-2026 - 31-Jan-2026
- EPSRC Centre for Doctoral Training in Engineering Hydrogen Net Zero EnerHy | Vinitha Jose, Jittu
- Patelli, Edoardo (Principal Investigator) Hamilton, Andrea (Co-investigator) Vinitha Jose, Jittu (Research Co-investigator)
- 01-Jan-2025 - 01-Jan-2029
- UKAEA Studentship - Uncertainty quantification for fusion: exploring novel techniques to quantify and propagate uncertainty in nuclear analysis through the design lifecycle
- Patelli, Edoardo (Principal Investigator)
- Student - Innes O'Donnell
- 01-Jan-2025 - 30-Jan-2027
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Contact
Professor
Edoardo
Patelli
Civil and Environmental Engineering
Email: edoardo.patelli@strath.ac.uk
Tel: 548 4682