Pau Herrero currently holds the position of Research Fellow in Biomedical Control Systems at Imperial College London within the Department of Electrical and Electronic Engineering. He is research co-director of 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., 2022, Personalized blood glucose prediction for Type 1 diabetes using evidential deep learning and meta-learning., Ieee Transactions on Biomedical Engineering, ISSN:0018-9294
et al., 2022, Enhancing self-management in type 1 diabetes with wearables and deep learning, Npj Digital Medicine, Vol:5, ISSN:2398-6352
et al., 2022, Identifying Continuous Glucose Monitoring Data Using Machine Learning, Diabetes Technology & Therapeutics, Vol:24, ISSN:1520-9156, Pages:403-408
Daniels J, Herrero P, Georgiou P, 2022, A Multitask Learning Approach to Personalized Blood Glucose Prediction, Ieee Journal of Biomedical and Health Informatics, Vol:26, ISSN:2168-2194, Pages:436-445
et al., 2022, IoMT-Enabled Real-time Blood Glucose Prediction with Deep Learning and Edge Computing, Ieee Internet of Things Journal