Dr Manuel Herrera
Senior Research Associate
Manuel Herrera is a Senior Research Associate in distributed intelligent systems at the Distributed Information and Automation Laboratory - DIAL - at the Institute for Manufacturing, University of Cambridge (UK). At DIAL, Manuel works for the Asset Management research group, where he explores novel methodologies for risk and resilience assessment of infrastructure systems within a framework of predictive analytics, complex networks, and graph signal processing.
Manuel successfully completed his PhD in Hydraulic Engineering at the Universitat Politècnica de València (Spain). For his PhD, he developed a methodology based on semi-supervised learning procedures, including spectral graph theory and agent-based systems, for an efficient management of urban water networks. After obtaining his PhD degree, Manuel did postdoctoral work at various institutions: Université Libre de Bruxelles (Belgium), Imperial College London (UK), and University of Bath (UK). His work focused on development of multidisciplinary optimization for computational mechanics, big data technologies for smart water networks, and machine learning approaches for the built environment. In September 2018, Manuel joined DIAL. His latest work is about AI-enabled management and maintenance of the UK national infrastructure. This is the case of his participation in granted projects on telecommunication systems, urban water, and 5G ports.
Currently, Manuel is the recipient of the Frank Hansford-Miller Fellowship in applied statistics, awarded by the Statistical Society of Australia (WA Branch). Manuel is also a fellow of the Royal Statistical Society and a member of organisations such as IEEE, IWA and the CSS. His interdisciplinary profile has proven to be successful in terms of the number and quality of publications; having a high academic impact. The number and impact of Manuel’s publications can be checked at his Google Scholar profile.
Google Scholar: https://scholar.google.co.uk/citations?user=Q2Vv-0AAAAAJ&hl=en
ORCID iD: https://orcid.org/0000-0001-9662-0017