Thursday 17th September 2020
Online seminar: mox.polimi.it/elenco-seminari/?id_evento=1978&t=763724
Link to recording:
Machine learning and biophysical modelling are very complementary approaches. The recent progress in computing power and available data makes it possible to develop accurate data-driven approaches for healthcare, while biophysical models offer a principled way to represent anatomy and physiology. In this talk, I will present research where we combine both methodologies in order to leverage their strengths. Different clinical applications in computational cardiology will be presented. This seminar is organized within the ERC-2016-ADG Research project iHEART - An Integrated Heart Model forthe simulation of the cardiac function, that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 740132). Contact: firstname.lastname@example.org
Maxime Sermesant is a researcher at Inria, the French research institute on informatics and mathematics, chair of « AI & Biophysics » at 3IA Côte d’Azur AI Institute, and head of « Multimodal Data Science » at IHU Liryc, Bordeaux. His research interests include biomedical image processing, organ modelling and machine learning. His main focus has been the application of patient-specific models of the heart to cardiac pathologies. He received his Diploma in General Engineering from Ecole Centrale Paris, France in 1999, his MSc from Ecole Normale Superieure de Cachan, France in 1999, and his PhD in Control, Signal and Image Processing from the University of Nice – Sophia Antipolis, France in 2003. From June 2003 to December 2005, he was a Research Fellow with the Cardiac MR Research Group, Guy’s Hospital, King’s College London, UK.