ESB Webinar Series - No. 19 - Physics based and Machine Learning Synergies in Human Heart Modelling

We will highlight how machine learning approaches, coupled with multiscale modeling, hold important opportunities to improve our understanding of cardiac tissue behaviour.

During this webinar attendees will learn about:
● How to quantify uncertainty in your data using Bayesian Inference and Hierarchical Modelling
● How to propagate uncertainty through your models using Gaussian Process Regression, Multi-fidelity modelling, and Active learning
● Discover the use of these tools towards sensitivity analyses, hypothesis testing, and prediction confidence estimations

The webinar is conducted by Dr. Mathias Peirlinck. Mathias Peirlinck is an Assistant Professor of BioMechanical Engineering at Delft University of Technology. He received his PhD in Biomedical Engineering from Ghent University and worked as a postdoctoral researcher at Stanford University's department of Mechanical Engineering. His lab's research focuses on the integration of multimodal experimental data, physics-based modeling, and machine learning techniques to understand, explore, and predict the multiscale behavior of the human heart, both in health and disease. Mathias received several awards and prizes including the Marie Sklodowska-Curie Seal of Excellence in 2019, the PhD AIG Price at Ghent University's Faculty of Engineering in 2022, and the Dutch Science Foundation Veni Talent Award in 2023. Mathias co-leads various national and international research consortia, including amongst others the Holland Hybrid Heart consortium, and VITAL, a Horizon Europe consortium focused on the advancement of multi-organ digital twins.
You can find Mathias on the website (https://peirlincklab.com/), Twitter/X (  / mpeirlinck  ) and Linkedin (  / mathiaspeirlinck  .