Hi, I’m Tiantian Wang. I’m currently a Ph.D. candidate in the Department of Advanced Computing Sciences (previously known as the Department of Data Science and Knowledge Engineering) at Maastricht University. Majored in mathematics and applied mathematics during my undergraduate and master's degrees, and now I’m still working on this specialty with a research focus on electrocardiographic imaging (ECGI). As a noninvasive technique, ECGI can reconstruct heart surface potentials (HSPs) from body surface potentials (BSPs) using a patient-specific torso-heart geometry derived from CT or MRI. For my Ph.D. project, we first investigated the influence of the regularization parameter on the accuracy of the reconstruction of epicardial potentials, then we provided a proof-of-principle Deep Neural Network which directly maps BSPs to HSPs, without the need to estimate a transfer matrix for the forward problem. Currently, we are working on improving the accuracy of deep learning results for ECGI and developing a standardized 2D atrium figure. Just like mathematics is only meaningful when it is applied to reality, we believe research on ECGI is only useful when it is applied to the clinic. For my future career development, I will work to strengthen the clinical and academic linkages while focusing on the clinical applications of research achievements, especially for ECGI.