Center for Statistics and Machine Learning

Applications in climate science, epidemiology, and transportation often require learning complex dynamics from large-scale spatiotemporal data.

Machine learning is a data driven endeavor, but real world systems are physical and mechanistic. In this talk we will review approaches to integrating machine learning with real world systems. Our focus will be on emulation (otherwise known as surrogate modeling).