Machine Learning for Sustainability
Policies for sustainable development entail complex decisions about balancing environmental, economic, and societal needs. Making such decisions in an informed way presents significant computational challenges. Modern AI techniques combined with new data streams have the potential to yield accurate, inexpensive, and highly scalable models to inform research and policy. In this talk, I will present an overview of my group's research on applying computer science techniques in sustainability domains, including poverty and food security.
Stefano Ermon is an Assistant Professor in the Department of Computer Science at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory and the Woods Institute for the Environment. Stefano's research is centered on 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.