School of Medicine
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David T. Paik
Instructor, Cardiovascular Institute
Bio Dr. David Paik is instructor working with Dr. Joseph Wu at Stanford Cardiovascular Institute. At Stanford, his focus is to utilize single-cell RNA-sequencing technology to elucidate patient-specific mechanisms of various cardiovascular diseases, characterize embryonic heart development, and optimize differentiation of iPSCs to subtypes of cardiovascular cells. Dr. Paik received his BA in Biochemistry and Molecular Biology at Boston University (2010) and PhD in Cell and Developmental Biology at Vanderbilt University (2015). At Vanderbilt, Dr. Paik investigated the endogenous cardiac repair mechanisms in the adult heart following ischemic injury such as myocardial infarction, with focus on the role of Wnt signaling pathway on coronary vessel formation and plasticity of endothelial cells during cardiac tissue repair. During his PhD training, Dr. Paik completed HHMI/VUMC Certificate Program in Molecular Medicine, where he was supervised by his clinical mentor Dr. Douglas Sawyer to interact with congestive heart failure patients and to bridge clinical sciences with basic and translational cardiovascular research. Dr. Paik is currently supported by the NIH NHLBI K99/R00 Pathway to Independence Award.
Postdoctoral Research Fellow, Cardiology
Bio I am a postdoc in the group of Alison Marsden, where I focus on cardiovascular blood flow simulations. As a visiting student researcher with Ellen Kuhl at Stanford, I became fascinated with the application of computer simulations to medical problems. I graduated from the Technical University of Munich with a Ph.D., where I co-founded a group dedicated to the prediction of cardiovascular diseases using simulation methods. Since then, my research mission has been to make simulations more accurate and more accessible for clinicians. During my doctoral studies, we enhanced mechanical models by studying the interaction between the myocardium and the pericardium. We demonstrated how model order reduction could be used to speed up model personalization from patient data, such as cine MRI or blood pressure measurements. We also showed how simulations could enable patient-specific therapy planning of radiofrequency catheter ablation in atrial fibrillation. I am currently working on an NIH-funded project to improve reproducibility in blood flow simulations with data curation methods. We are developing a public repository of patient-specific simulations where other scientists can submit their simulations and automatically regain feedback. My long-term vision is to develop combined physics-based and data-based approaches to enable personalized therapies for the cardiovascular system.