Tuesday, January 12, 2016, 4:30 - 6:00 pm at the Arrillaga Alumni Center, Fisher Conference rooms, Stanford University. Registration is required for this event. Please click here to register.
Stefano Ermon, Assistant Professor, Computer Science
Talk title: Computational Approaches to Sustainable Energy
Modern approaches to sustainable energy 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 new automatic planning and sequential decision-making methods we developed to improve the efficiency of battery systems, and introduce novel machine learning models for the analysis of high-throughput data in materials science to accelerate the discovery of new fuel-cell materials.
Stefano Ermon is an Assistant Professor in the Department of Computer Science at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory. 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.