CMU talk: Set estimation for statistical inference in brain imaging and active sensing

Machine Learning Lunch (http://www.cs.cmu.edu/~learning/)
Speaker: Prof. Aarti Singh
Venue: GHC 6115
Date: Monday, October 26, 2009

Set estimation for statistical inference in brain imaging and active sensing

Inferring spatially co-located regions of interest is an important problem in several applications, such as identifying activation regions in the brain or contamination regions in environmental monitoring. In this talk, I will present multi-resolution methods for passive and active learning of sets that aggregate data at appropriate resolutions, to achieve optimal bias and variance tradeoffs for set estimation. In the passive setting, we observe some data such as a noisy fMRI image of the brain and then extract the regions with statistically significant brain activity. Active setting, on the other hand, involves feedback where the location of an observation is decided based on the data observed in the past. This can be used for rapid extraction of set estimates, such as a contamination region in environmental monitoring, by designing data-adaptive spatial survey paths for a mobile sensor. I will describe a method that uses information gleaned from coarse surveys to focus sampling around informative regions (boundaries), thus generating successively refined multi-resolution set estimates.

I will also discuss some current research directions which aim at efficient extraction of spatially distributed sets of interest by exploiting non-local dependencies in the data.