Alejandro Frangi
Alejandro Frangi | |
|---|---|
| Occupation | Engineer |
| Engineering career | |
| Employer(s) | University of Manchester |
| Awards | FREng |
Professor Alejandro (Alex) Frangi FREng is an Argentinian engineer and scientist and a pioneered in computational medicine. He specialises in the engineering of machine learning for medical image analysis and modelling. He has published over 850 peer-reviewed articles in his field.[1]
Education
[edit]He moved to Barcelona in 1991, where he obtained his undergraduate degree in Telecommunications Engineering from the Technical University of Catalonia in 1996. Frangi obtained grants from both Dutch Ministry of Economic Affairs and CIRIT to pursue his PhD.[2]
Career
[edit]Early in his career, Frangi was a visiting researcher at Imperial College, London. He also worked at Philips Medical Systems BV, The Netherlands.[2] He currently serves as Bicentennial Turing Chair in Computational Medicine at the University of Manchester. Frangi also serves as chair in Emerging Technologies for The Royal Academy of Engineering. He also has visiting positions at KU Leuven, Shenzhen University and Beijing Institute of Technology.
Research
[edit]Frangi's most notable research and development was in the field of silico clinical trials. These clinical trials can be completed digitally and can also model and predict outcomes in the form of a computerised simulation.[3] In April 2023, Frangi's research project INSILICO received a grant from the European Research Council. The research involves simulating clinical trials using "digital twins."[4] It is hoped that technology breakthroughs in this field could reduce both the time and cost of some trials.[5]
References
[edit]- ^ "Alejandro F Frangi". ResearchGate.
- ^ a b "Alejandro Frangi bio". Alan Turing Institute.
- ^ Abadi E, Segars WP, Tsui BM, Kinahan PE, Bottenus N, Frangi AF, et al. (July 2020). "Virtual clinical trials in medical imaging: a review". Journal of Medical Imaging. 7 (4): 042805. doi:10.1117/1.JMI.7.4.042805. PMC 7148435. PMID 32313817.
- ^ "Pioneering safer, cheaper and quicker clinical trials with AI 'digital twins'". Leeds University. April 20, 2023.
- ^ "Clinical trials are too slow and too costly—here is how to fix them". The Economist. May 24, 2023.