Speakers

Dr. Jonathan Sadeghi is a Research Engineer at Bosch (formerly FiveAI), based in Bristol. Currently his work is focused towards developing AI systems to help build driverless cars. He obtained his PhD in Engineering from the University of Liverpool (2020), focusing on uncertainty quantification and machine learning. His research interests span the intersection of computer vision and probabilistic machine learning with applications to autonomous vehicles. https://jcsadeghi.github.io/ 

Dr Silvia Tolo gained an M.Sc. in Energy and Nuclear Engineering from the University of Bologna, and subsequently collaborated with the Institute for Risk and Uncertainty at the University of Liverpool, where she was awarded a PhD. She is currently undertaking research within The Resilience Engineering Research Group at the University of Nottingham on the development of theoretical and computational tools for the efficient modelling of complex systems.

Peter Hristov holds a bachelor’s degree in aerospace engineering (2014) and a PhD in computational engineering (2018) from the University of Liverpool. Since 2018 Peter has been working as a post-doctoral research assistant in the areas of computational engineering and uncertainty quantification, at the “Institute for Risk and Uncertainty” in the UK. In 2022 he won a fellowship with the GATE Institute in Bulgaria for the “Advancing uncertainty-aware digital twins” (AUDiT) project. Peter’s research focuses on model reliability and the developments of industrially-applicable computational methods for uncertainty quantification. His research interests span the fields of computational and numerical modelling, uncertainty quantification, computer model-based certification and aerospace design.

Nick Gray is a postdoctoral research assistant at the University of Liverpool’s Institute for Population Health. He is currently researching the communication of risk and uncertainty in medical AI. His PhD is entitled The Importance of Risk and Uncertainty in Humane Algorithms and has research interests including application of imprecise probabilities in machine learning, uncertainty in medical diagnosis and the ethics of machine learning.

Maria Chiara Leva is the Lead of the Human factors in Safety and Sustainability (HFISS) research group in Technological University Dublin and a Senior Lecturer in the School of Environmental Health for the same institution. She is a visiting research Fellow in the Centre for Innovative Human systems in Trinity College Dublin. She is the co-founder of Tosca Solutions (www.toscasolutions.com) a Spin out campus company based in NDRC and Trinity College Dublin to offer support for implementing risk management tools customised specifically to the needs of highly regulated environments. Her area of Expertise is Human factors and Safety Management Systems. Chiara holds a PhD in Human factors conferred by the Polytechnic of Milano Department of Industrial Engineering. She is the former chair of The Irish Ergonomics Society and current co-chair of the technical committee for Human factors in the European Safety and Reliability Association.

Dr. Sifeng Bi is a Strathclyde Chancellor’s Fellow and holds a Lecturer position at the Aerospace Centre of Excellence in the Department of Mechanical & Aerospace Engineering at the University of Strathclyde. His research topics are uncertainty quantification, stochastic model updating, numerical verification and validation, especially in the application of complex aerospace engineering dynamics. In particular, he focuses on probabilistic techniques such as advanced Monte Carlo simulation, approximate Bayesian computation, and global sensitivity analysis, with the consideration of uncertainties. He sits at the AIAA Technical Committee of Non-Deterministic Approaches. He is the Associate Editor of the ASCE-ASME Journal of Risk and Uncertainty, and Guest Editor of the international journal Mechanical Systems and Signal Processing. He is a Senior Member of AIAA.  

Yu Chen is currently a Research Assistant at University of Strathclyde. During his PhD at University of Liverpool, funded by the EU H2020 MSCA project URBASIS, his research work mainly revolves around developing robust and knowledge-informed Deep Learning frameworks, dedicated to characterisation, propagation, and quantification of uncertainty embedded in the bad data (e.g. scarce, incomplete, or imprecise) through an efficient computational pipeline.