Decision Making and Inference under Limited Information and Large Dimensionality
|Event Date:||April 2, 2014|
|Speaker Affiliation:||Cornell University|
|Sponsor:||Prospective ECE faculty member|
|Time:||10:30am, Reception 11:30am
|Contact Name:||Professor Jeff Siskind
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 a PhD candidate in Computer Science at Cornell University, where he started in the Fall of 2008. His research interests include combinatorial search, probabilistic inference, machine learning, and optimization. He works at the Intelligent Information Systems Institute Institute (IISI) and at the Institute for Computational Sustainability (ICS) under the joint supervision of Carla Gomes and Bart Selman. Before coming to Cornell, he graduated summa cum laude with a BS and MS in Electrical Engineering from the University of Padova. Stefano has (co-)authored nearly 20 publications, and has won several awards, including two Best Student Paper Awards, one Runner-Up Prize, and a McMullen Fellowship.