Dr Manuel Herrera
Manuel Herrera is MSc in Statistics by the Universidad de Valladolid (Spain) and PhD in Hydraulic Engineering and Environmental Studies by the Universitat Politècnica de València (Spain), where he also worked as part-time lecturer at the Applied Mathematics Department.
After obtaining his PhD degree in 2011, 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, Complex Networks, and Time-series Data Mining approaches for Engineering and the built environment. In September 2018, Manuel joined DIAL, at the Institute for Manufacturing of the University of Cambridge (UK), as Research Associate in Distributed Intelligent Systems. His main research is on collective intelligence and dynamic complex networks modelling for optimising the topology configuration and resilience of telecommunication networks.
Additional Manuel’s research interests lie on developing novel Data Mining methods, investigating the use of Graph Databases, and implementing new solutions in the topic of Urban Informatics for smart and resilient cities. As outcome of his research, Manuel is co-author of a large number of JCR indexed journal papers, several book chapters in international publishers, 1 book co-edited, and more than 100 conference papers in international meetings. He was granted with outstanding reviewer recognition by Water journal (MDPI publisher, 2017). Also for this journal, Manuel successfully guest-edited a special issue on “Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems” (2017-18). He is actively involved on leading the organisation of another special issues, all of them related to Urban Informatics and Critical Infrastructures. Manuel has been a supervisor of an Electrical Engineering MSc thesis and two Doctoral Dissertations in Water Engineering; he is currently supervising another two ongoing PhD theses.
Google Scholar: https://scholar.google.co.uk/citations?user=Q2Vv-0AAAAAJ&hl=en
ORCID iD: https://orcid.org/0000-0001-9662-0017