Faculty of Engineering
 

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Silvia Tolo

Assistant Professor, Faculty of Engineering

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Biography

I am an Assistant Professor in the Department of Mechanical, Materials and Manufacturing Engineering at the University of Nottingham, and a member of the university's Resilience Engineering Research Group. My research focuses on modelling and simulating complex engineered systems, with the aim of improving their safety, reliability and resilience.

Before taking up my current position, I worked within the same research group as a Senior Research Fellow on the NxGen Project, funded by the Lloyd's Register Foundation. During this fellowship, I focused on the development of the Dynamic and Dependent Tree (D2T2) methodology and created associated computational tools for advanced system safety analysis. These methods have been applied across sectors including nuclear and aerospace engineering, through collaborations with both industrial and academic partners.

I hold both a Master's and a Bachelor's degree in Energy and Nuclear Engineering from the University of Bologna. I later completed my PhD in Risk and Uncertainty at the University of Liverpool, working on computational approaches for risk assessment under uncertainty using Bayesian and credal networks, and contributing to the development of the OpenCossan software with the implementation of original tools and methodologies.

Since completing my PhD, I have continued to combine simulation, probabilistic modelling and data-driven techniques across a range of engineering applications. My work has also included the development of theoretical and computational tools for uncertainty quantification in digital twins and machine learning.

I have a long-standing interest in international collaboration and have worked with research teams in India, China, Belgium and the United States. Alongside my research, I contribute to the academic community through publications, invited talks, peer review, and the supervision and mentoring of early-career researchers.

Expertise Summary

My work sits at the intersection of simulation, probabilistic modelling and system safety. I specialise in methods that help us understand how complex engineered systems behave, how failures can propagate, and how resilience can be improved under uncertainty.

My expertise includes:

  • System Safety and Reliability: development and application of advanced safety models, as well as of fault and event trees, dynamic dependency modelling, Bayesian and credal networks, and other probabilistic representations of complex systems.

  • Computational Modelling and Simulation: advanced Monte Carlo methods, discrete-event simulation, numerical optimisation and surrogate modelling to support predictive and scenario-based analyses.

  • Uncertainty Quantification: theoretical and computational approaches for handling uncertainty in engineering models, digital twins and machine learning systems.

  • Software and Tool Development: creation of bespoke computational tools in C++, Java and MATLAB.

  • Application Domains: safety-critical environments such as nuclear and aerospace systems, as well as broader engineering contexts where robust modelling and decision support are essential.

Across these areas, my work aims to develop methods and tools that support reliable, interpretable and efficient analysis of complex systems.

Teaching Summary

Modules I am currently involved with:

  • MMME1050 - Integrated Project and Maths 1 Systems Engineering component

  • EEEE1006 - Aerospace Electronic Engineering and Computing Introduction to MATLAB component

  • MMME3050 - Management and Professional Practice System Reliability component

Research Summary

My current research focuses on improving asset management and system safety in complex engineered systems. I develop advanced models that capture the dynamic behaviours and interdependencies within… read more

Selected Publications

Current Research

My current research focuses on improving asset management and system safety in complex engineered systems. I develop advanced models that capture the dynamic behaviours and interdependencies within systems, helping to better predict failures and optimize maintenance strategies. By addressing the limitations of traditional safety and reliability tools, my work enables more accurate assessments of system degradation and identifies vulnerabilities before they lead to critical failures. The goal is to improve the lifespan, reliability, and performance of critical assets, particularly in high-risk environments like nuclear and aerospace industries. I integrate techniques such as Markov Models, Petri Nets and Monte Carlo simulations to model system performance under uncertainty, supporting data-driven decision-making for more resilient systems.

Past Research

My earlier work laid the foundation for understanding how uncertainty impacts system behaviour, and more recently, I've expanded this to focus on the resilience of cyber-physical systems. I worked on applying machine learning for early fault and failure detection in complex engineering systems. This research focused on pattern recognition techniques to identify potential issues before they escalate, ensuring rapid response and effective mitigation. Additionally, I explored the use of digital twin models, with a particular emphasis on quantifying the uncertainty surrounding these models. I focused on how surrogate models could be used within digital twins, addressing the challenge of uncertainty in simulations to improve their predictive accuracy and decision support in real-world applications.

Faculty of Engineering

The University of Nottingham
University Park
Nottingham, NG7 2RD



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