Stefano Ermon: 2016 Plenary Session

9:30am

Modern approaches to global sustainability challenges call upon a wide range of scientific disciplines. A common theme, however, is the availability of large amounts of data. In many application domains there is a growing need to automatically learn models, make inferences, and optimally control and manage systems based on data-driven models.These are problems that require new methods in machine learning, probabilistic inference, and decision-making under uncertainty, and where new computational approaches can have a profound impact. In this talk, I will discuss examples in sustainable development and new materials discovery for energy applications.

Stefano Ermon is currently an Assistant Professor in the Department of Computer Science at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory. He completed his PhD in computer science at Cornell in 2015. His research interests include techniques for scalable and accurate inference in graphical models, statistical modeling of data, large-scale combinatorial optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging field of computational sustainability. Stefano has won several awards, including two Best Student Paper Awards, one Runner-Up Prize, and a McMullen Fellowship.