Professor Oleg Aslanidi
Professor in Biophysical Cardiac Modelling
Contact details
Biography
Oleg Aslanidi is a Professor in the School of Biomedical Engineering & Imaging Sciences and Course Director of Biomedical Engineering BEng/MEng.
Research areas:
Biophysical modelling, cardiac electrophysiology, atrial fibrillation, thrombogenesis, artificial intelligence
Research
Centre for Doctoral Training in Digital Twins for Healthcare
DT4Health brings together a world-class multidisciplinary team of supervisors to train future innovation leaders to articulate and materialise the Digital Twin vision in healthcare.
News
Students rate Biomedical Engineering programme highest at King's and across London
The Biomedical Engineering BEng/MEng programme has achieved an overall average of 89% positive responses in the National Student Survey (NSS) for 2024.
Biomedical Engineering scores highest rating for an Undergraduate Programme at King's
The Biomedical Engineering BEng/MEng programme has achieved an overall average of 87% positive responses in the National Student Survey (NSS) for 2023.
AI approach could predict treatment strategy for arrhythmia patients
The proposed approach is unique in combining patient imaging, image-based modelling and artificial intelligence (AI)
Research
Centre for Doctoral Training in Digital Twins for Healthcare
DT4Health brings together a world-class multidisciplinary team of supervisors to train future innovation leaders to articulate and materialise the Digital Twin vision in healthcare.
News
Students rate Biomedical Engineering programme highest at King's and across London
The Biomedical Engineering BEng/MEng programme has achieved an overall average of 89% positive responses in the National Student Survey (NSS) for 2024.
Biomedical Engineering scores highest rating for an Undergraduate Programme at King's
The Biomedical Engineering BEng/MEng programme has achieved an overall average of 87% positive responses in the National Student Survey (NSS) for 2023.
AI approach could predict treatment strategy for arrhythmia patients
The proposed approach is unique in combining patient imaging, image-based modelling and artificial intelligence (AI)