Imperial College London

Dr Pau Herrero

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Visiting Researcher
 
 
 
//

Contact

 

p.herrero-vinias

 
 
//

Location

 

B422Bessemer BuildingSouth Kensington Campus

//

Summary

 

Summary

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.

Publications

Journals

Hernandez B, Herrero-Viñas P, Rawson TM, et al., 2021, Resistance trend estimation using regression analysis to enhance antimicrobial surveillance: a multi-centre study in London 2009-2016, Antibiotics, Vol:10, ISSN:2079-6382, Pages:1-16

Leon-Vargas F, Martin C, Garcia-Jaramillo M, et al., 2021, Is a cloud-based platform useful for diabetes management in Colombia? The Tidepool experience, Computer Methods and Programs in Biomedicine, Vol:208, ISSN:0169-2607

Daniels J, Herrero P, Georgiou P, 2021, A Multitask Learning Approach to Personalised Blood Glucose Prediction., Ieee J Biomed Health Inform, Vol:PP

Zhu T, Li K, Herrero P, et al., 2021, Deep Learning for Diabetes: A Systematic Review, Ieee Journal of Biomedical and Health Informatics, Vol:25, ISSN:2168-2194, Pages:2744-2757

Rawson TM, Wilson RC, O'Hare D, et al., 2021, Optimizing antimicrobial use: challenges, advances and opportunities, Nature Reviews Microbiology, Vol:19, ISSN:1740-1526, Pages:747-758

More Publications