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I recently completed my Ph.D. at the University of California, San Diego where I was an NSF Fellow in Vision and Learning in Humans and Machines. I have since moved to a postdoctoral position in the Biomagnetic Imaging Lab at the University of California, San Francisco. Currently, I am interested in Bayesian inference as applied to the problem of finding sparse representations of signals using overcomplete (redundant) dictionaries of candidate features. In contrast to the Moore-Penrose pseudoinverse, which produces a representation with minimal energy or high diversity, I'm concerned with finding inverse solutions using a minimum number of nonzero expansion coefficients (maximal sparsity). A particularly useful application of this methodology is to the source localization problem that arises in neuroelectromagnetic imaging and brain computer interfacing (BCI). Here the goal is to convert an array of scalp sensor measurements into an estimate of synchronous current activity within the brain which can then be used for classifying brain states. I'm also looking at sparse coding problems associated with the visual cortex.
Sparse Estimation with Structured Dictionaries
as author at Video Journal of Machine Learning Abstracts - Volume 2,
Latent Variable Sparse Bayesian Models
as author at Workshop on Sparsity in Machine Learning and Statistics, Cumberland Lodge 2009,
Approximation and Inference using Latent Variable Sparse Linear Models
as author at NIPS Workshop on Approximate Bayesian Inference in Continuous/Hybrid Models, Whistler 2007,