Pau Herrero currently holds the position of Visiting Researcher within the Department of Electrical and Electronic Engineering. In particular, he collaborates with the Metabolic Technology Laboratory in the Centre for Bio-Inspired Technology, a multi-disciplinary group that aims to tackle pressing healthcare problems through the utilisation of engineering and data science solutions, with a particular emphasis on transferring these technologies to society.
His research is focused on developing automated drug delivery systems and decision support systems to address open problems in the fields of diabetes and infectious diseases management. He has been Principal Investigator of an H2020 project aiming at developing a diabetes self-management system, which has received the category of 'Tech Ready' by the European Commission's Innovation Radar.
Dr. Herrero graduated with a 1st Class Honours in Industrial Engineering in 2001 from University of Girona and obtained a double-degree Ph.D. on Automation and Applied Informatics in 2007 from Université Angers and University of Girona (Cum Laude). He also spent one year as a postdoctoral researcher at The Doyle Group (University of California Santa Barbara).
He is a member of the Centre for Antimicrobial Optimisation, which aims to optimising antimicrobial use to address the global challenge of antimicrobial resistance. He also serves on the United Kingdom Interval Methods Working Group technical committee, a working group aiming to bring together researchers from UK and abroad working on set-membership methods.
et al., 2023, Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes., J Diabetes Sci Technol
et al., 2023, GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks., Ieee J Biomed Health Inform, Vol:PP
et al., 2023, Safety and efficacy of an adaptive bolus calculator for Type 1 diabetes: a randomised control cross over study, Diabetes Technology and Therapeutics, Vol:25, ISSN:1520-9156, Pages:414-425
et al., 2023, IoMT-Enabled Real-Time Blood Glucose Prediction With Deep Learning and Edge Computing, Ieee Internet of Things Journal, Vol:10, ISSN:2327-4662, Pages:3706-3719
et al., 2023, Personalized blood glucose prediction for Type 1 diabetes using evidential deep learning and meta-learning., Ieee Transactions on Biomedical Engineering, Vol:70, ISSN:0018-9294, Pages:193-204