Peter J Ramadge, Princeton University

Professor Princeton, New Jersey ramadge@princeton.edu Office: (609) 258-4645

Bio/Research

My research interests are in the areas of signal processing and machine learning. I work on a variety of fundamental problems (including boosting, adaptive signal processing, and learning from data) and in a variety of application domains (including fMRI analysis, adaptive control, optimization o...

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Bio/Research

My research interests are in the areas of signal processing and machine learning. I work on a variety of fundamental problems (including boosting, adaptive signal processing, and learning from data) and in a variety of application domains (including fMRI analysis, adaptive control, optimization of queuing systems, and video analysis, annotation, and search). My recent research on function Magnetic Resonance Imaging (fMRI) analysis has centered on developing efficient algorithms for extracting information from large fMRI data sets. For example, in collaboration with neuroscience colleagues, we have developed algorithms for functionally aligning the cortices of multiple subjects using the fMRI data measured during movie viewing. To do so, we used have used two distinct alignment metrics: the correlation of corresponding time series and a metric based on aligning intra-subject functional connectivity. In other recent work we have examined the problem of spatially informed voxel selection from fMRI data, using methods based on spatially regularized boosting and level-set estimation. In ongoing work we are examining the issue of tracking the presence of people in a movie stimulus over time, based on fMRI data collected during the viewing of the stimulus. My research group is also currently investigating problems of signal representation using trees and wavelet constructions, semi-supervised clustering of data, learning patterns in data, manifold regularization and estimation, and online learning. I am an active member of the neuroimaging analysis methods group at Princeton, the recipient of several teaching awards, an IBM faculty development award, and an IEEE best paper award. I am a fellow of the IEEE and a member of the Society for Industrial and Applied Mathematics (SIAM).

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