Interdisciplinary research and education for a computational future
Abstract: In this talk I will discuss challenges and needs for tackling some of society's most pressing problems across science, engineering and medicine. I'll make the case for "predictive data science" — recognizing that in high-consequence decisions, methods based on data alone are not enough. Rather we need a synergistic combination of modern data-driven and more classical physics-based perspectives. In our excitement to embrace machine learning and artificial intelligence, it is critical that we not forget about the predictive power, the interpretability, and the domain knowledge associated with physics-based models. I will also discuss implications and needs for interdisciplinary education.