Resilience Engineering Research Group
 

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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

The main focus of the current research lies with the development and integration of numerical techniques for the safety assessment of complex engineering systems. The aim is very much to overcome the… read more

Selected Publications

Current Research

The main focus of the current research lies with the development and integration of numerical techniques for the safety assessment of complex engineering systems. The aim is very much to overcome the limitations of currently available techniques promoting the shift towards more realistic modelling of real-world complex installations, capturing the dynamic features and dependencies underlying modern systems. The range of methodologies involved includes, but is not limited to, Binary Decision Diagrams, Markov Models, Petri Nets, Fault and Event Trees.

Past Research

In 2016 I was awarded a Ph.D. from the University of Liverpool, where I collaborated with the Institute for Risk and Uncertainty, focusing on the implementation of risk assessment techniques and uncertainty modelling, with particular focus on engineering installation subject to natural hazards adopting Bayesian and Credal Networks. In 2017 I partecipated in the SMART project (EPSRC funded) in collaboration with the Bhabha Atomic Research Centre (BARC) in India, focusing on the development of machine learning techniques for the on-line monitoring of nuclear power plants. In 2018 I collaborated with the Virtual Engineering Centre of the University of Liverpool and several major industrial partners on the Digital Reactor Design Programme (BEIS funded), focusing my research on the modelling of uncertainty in digital twins. In 2019 I started my collaboration with the Resilience Engineering group of the University of Nottingham, working on the Nuclear Resilience (NuRes) project and investigating modelling frameworks for the simulation of cyber-physical systems under threat.

Resilience Engineering Research Group

The University of Nottingham
Pavement Research Building
University Park
Nottingham, NG7 2RD


telephone: +44 (0)115 84 67366
email: r.remenyte-prescott@nottingham.ac.uk