Computational Medicine in the Heart, Integrated Training Program

Cardiovascular (CV) diseases are rising causes of morbidity and mortality worldwide. There is excitement that computational medicine, an emerging field combining engineering disciplines with the life sciences, will enable scientific and clinical breakthroughs and accelerate bench-to-bedside translation. However, few training programs exist in this field, and so trainees often learn ad hoc. We seek funding for a new multidisciplinary T32 program in Computational medicine in the Heart: Integrated training Program (CHIP) at Stanford. CHIP will provide cutting-edge training for 3 post-PhD, -MD or -MD/PhD fellows annually, each undergoing 2 years of training at the intersection of engineering, CV physiology and medicine. Trainees will pursue a cutting-edge research project mentored by faculty with complementary expertise in engineering and the life sciences, and select didactic courses to build expertise, grow professionally, and develop community. The forward-looking vision of CHIP addresses key priorities of several National Agencies and fills current gaps in interdisciplinary training. Stanford CHIP leverages faculty and resources at top-ranked Schools of Engineering, Medicine and Humanities and Sciences. The T32 is co-directed by a physician-engineer and an engineer-physiologist, bringing 38 faculty from 13 Departments and Divisions. Key support is provided by the inter-disciplinary Cardiovascular Institute (CVI) and the Institute for Computational and Mathematical Engineering (ICME) at Stanford. Faculty will provide trainees with research opportunities in CV science spanning cell-to-organ and bench-to-bedside, as well as computational science, clinical care, and therapeutic innovation. The faculty are highly collaborative and have exceptional track records of launching trainees into independent scientific careers. Trainees will not be required to have backgrounds in both engineering and life sciences. The T32 will provide tailored teaching of CV science to engineers, engineering to life-science fellows, and advanced cross-disciplinary topics to each. Core didactics also include ethics, the responsible conduct of science, methods to ensure reproducibility. Evaluation will be both constructive and bidirectional between trainees and faculty. In summary, the CHIP T32 at Stanford provides post-MD, post-PhD and post-MD/PhD graduates with world-class training at the intersection of bioengineering, CV science and medicine. The T32 is well positioned for success due to the co-location of these top-tier resources on a single campus in Silicon Valley, a hub of innovation in data science, artificial intelligence and therapy. Our aspirational goal is that CHIP graduates will become global scientific leaders at the cusp of engineering, physiology and medicine.