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New Directions in
Statistical Learning for Meaningful and Reproducible fMRI Analysis

NIPS 08 workshops, December 13, Whistler, Canada

Organizers

Melissa Carroll is a graduate student in Computer Science and Neuroscience at Princeton University, advised by Robert Schapire. She earned a BA in Psychology from Binghamton University, an MS in Computer Science from Pace University, and an MA in Computer Science from Princeton University. From 1999-2004, she worked as a Research Data Specialist in the Psychiatry department at Weill Medical College of Cornell University, analyzing clinical and neuroimaging data. Since 2004, her graduate work has focused on the use of statistical learning techniques for neuroscientific discovery from fMRI data. In 2007 and 2008, she interned with the Biometaphorical Computing Group at the IBM TJ Watson Research Center, focusing on analysis of fMRI data.
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Irina Rish is a research staff member at the Biometaphorical Computing Department which is a part of the Computational Biology Center at the IBM T. J. Watson Research Center. She received an M.S. in applied mathematics from Moscow Gubkin Institute, Russia, and a Ph.D. in computer science from the University of California, Irvine. Dr. Rish's primary research interests are in the areas of probabilistic inference, machine learning, and information theory, and their applications to large-scale data analysis problems in biology and neuroscience. Her current research focus is on applyng machine-learning techniques to neuroscience, and particularly on statistical analysis of fMRI data using sparse regression and dimensionality reduction. In the past, she has also worked on efficient approximations of probabilistic inference in Bayesian networks, active learning, collaborative prediction, sparse regression and sparse matrix factorization, and their applications to autonomic computing (self-managing computer systems). She over 40 conference and journal publications on the above topics. Dr. Rish taught several machine learning courses at the Electrical Engineering and Computer Science departments of Columbia University as an adjunct professor, and co-organized several machine-learning workshops, including ICML workshop in 2000, NIPS workshops in 2003, 2005 and 2006, ECML workshop in 2006, and, most recently, ICML 2008 workshop on Sparse Regression and Variable Selection.
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Francisco Pereira is a post-doctoral researcher at the Princeton Neuroscience Institute, working with Matthew Botvinick, Ken Norman and David Blei. He was a graduate student under Tom Mitchell and Geoff Gordon at Carnegie Mellon University, and graduated with a PhD in Computer Science/Neural Basis of Cognition in December 2007. His broad area of research is the development and adaptation of machine learning methods for the purpose of answering scientific questions and facilitating discovery in cognitive neuroscience. He was the lead organizer on the 2006 NIPS Workshop on New Directions on Decoding Mental States from fMRI Data.
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Guillermo Cecchi received an education in Physics (MSc, University of La Plata, Argentina, 1991), Physics and Biology (PhD, The Rockefeller University, 1994-1999), and Imaging in Psychiatry (Postdoctoral Fellow, Cornell University 2000-2001). In 2001 he joined IBM Research to be part of the Biometaphorical Computing project, where he has been working on computational approaches to brain function and systems biology. His research interests have covered diverse aspects of theoretical biology, including Brownian transport, molecular computation, spike reliability in neurons, song production and representation in songbirds, statistics of natural images and visual perception, statistics of natural language, and brain imaging. Recently, he has pioneered the use of statistical network theory for the analysis and modeling of functional brain networks.
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