Stefano Ermon: 2015 AI Workshop


Thursday April 30, 2015
Location: Fisher Conference Center, Arrillaga Alumni Center

"Decision Making and Inference under Limited Information and High Dimensionality"


Statistical inference in high-dimensional probabilistic models (i.e., with many variables) is one of the central problems of statistical machine learning and stochastic decision making. To date, only a handful of distinct methods have been developed, most notably (MCMC) sampling, decomposition, and variational methods. In this talk, I will introduce a fundamentally new approach based on random projections and combinatorial optimization. Our approach provides provable guarantees on accuracy, and outperforms traditional methods in a range of domains, in particular those involving combinations of probabilistic and causal dependencies (such as those coming from physical laws) among the variables. This allows for a tighter integration between inductive and deductive reasoning, and offers a range of new modeling opportunities. As an example, I will discuss an application in the emerging field of Computational Sustainability aimed at discovering new fuel-cell materials where we greatly improved the quality of the results by incorporating prior background knowledge of the physics of the system into the model.


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 2014. 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 (co-)authored over 20 publications, and has won several awards, including two Best Student Paper Awards, one Runner-Up Prize, and a McMullen Fellowship.