Computational modeling of electrocardiology (EC) has a great potential for use in improved diagnosis and prognosis of cardiac arrhythmia. However, recent computational methods usually suffer from three major limitations that hinder their clinical use: lack of efficient model personalization strategies; high computational demand from the EC solver; lack of good trade-off between the simplification of cellular ionic models and the demand on keeping enough biophysical details. In this talk we address some challenges mentioned above by the methods of derivative-based optimization and model order reduction, we also pose challenging problems in cardiology that request merged contribution from a wide variety of disciplines including but not limited to mathematics, biomedical engineering, and medicine.

杨欢欢，2015年博士毕业于美国埃默里大学数学与计算机科学系计算数学专业，2015-2017年在佛罗里达州立大学科学计算系从事博士后研究工作，2017年8月加入汕头大学数学系。研究方向包括偏微分方程数值解的快速算法以及在心血管疾病预控等方面的应用。