Pau Herrero Viñas received the M.Eng. degree in Industrial Engineering in 2001 from University of Girona(Catalonia-Spain) and the Ph.D. degree in 2006 from University of Angers (France) and University of Girona.
After receiving his PhD, Pau moved to Doyle’s Group at the Chemical Engineering Department of University of California Santa Barbara (USA), where he spent one year as a postdoctoral research fellow working on an artificial pancreas project in collaboration with Sansum Diabetes Research Institute (USA).
He then spent the following two years at Hospital de Sant Pau (Catalonia–Spain) working as research fellow for the Biomedical Research Networking center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN) leading different eHealth projects for the prevention of type 2 diabetes and management of type 1 diabetes.
Pau is currently a research fellow within the Centre for Bio-Inspired Technology at the Department of Electrical & Electronic Engineering.
His main research interest lies in the field of diabetes technology and antimicrobial resistance (AMR). In field of diabetes management, he is involved in the development of a Bio-inspired Artificial Pancreas. Another ongoing research he is involved in is on the development of a decision support system for type 1 diabetes management based on Case Based Reasoning (CBR) technology.
His research in the field of AMR consists of developing of a point-of-care decision support system to optimize antimicrobial therapy in intensive and secondary care. This system consists of a mobile app that automatically pulls patient data from hospital servers and uses machine learning techniques to help clinicians to make the right choices for antibiotic therapy from the beginning of the treatment. Such technology is currently being tested at the Imperial College Healthcare NHS Trust.
et al., 2016, Case-Based Reasoning for Insulin Bolus Advice., J Diabetes Sci Technol, Vol:11, Pages:37-42
et al., 2018, Unannounced Meals in the Artificial Pancreas: Detection Using Continuous Glucose Monitoring., Sensors (basel), Vol:18
et al., 2018, Automatic Adaptation of Basal Insulin Using Sensor-Augmented Pump Therapy., J Diabetes Sci Technol, Vol:12, Pages:282-294
et al., 2017, A systematic review of clinical decision support systems for antimicrobial management: are we failing to investigate these interventions appropriately?, Clinical Microbiology and Infection, Vol:23, ISSN:1198-743X, Pages:524-532
et al., 2017, Enhancing automatic closed-loop glucose control in type 1 diabetes with an adaptive meal bolus calculator - in silico evaluation under intra- day variability, Computer Methods and Programs in Biomedicine, Vol:146, ISSN:0169-2607, Pages:125-131