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Bayesian Dynamic Modeling: Sharing Information Across Time and Space, UW CSE, Nov. 8
November 8, 2012 @ 3:30 pm - 5:00 pm
Bayesian Dynamic Modeling: Sharing Information Across Time and Space, UW CSE
When: Thursday, November 8, 2012, 3:30pm
Where: CSE 520 Colloquium joint with E-Science Institute
What: In this talk we will highlight some of the benefits and challenges associated with harnessing the temporal structure present in many datasets. We focus on Bayesian dynamic modeling approaches, and in particular, the idea of sharing information across time and “space”, where space generically refers to the dimensions of the time series. We exploit nonparametric and hierarchical models to capture repeated patterns in time and similar structure in space, enabling the modeling of complex and high-dimensional time series. Applications of such approaches are quite diverse, and in this talk we will demonstrate this by touching upon our work in the tasks of speaker diarization, analyzing human motion, detecting changes in volatility of stock indices, parsing EEG, word classification from MEG, and predicting rates of violent crimes in DC and influenza rates in the US.
Who: Emily Fox is an Assistant Professor in the Department of Statistics at the University of Washington, having joined in 2012 from a prior position at the Wharton Department of Statistics, University of Pennsylvania. From 2009-2011 she was a postdoc in the Duke Statistical Science Department, and received her S.B., M.Eng., E.E. and Ph.D. in EECS at MIT. Her doctoral thesis was awarded the 2009 Leonard J. Savage Thesis Award in Applied Methodology and the 2009 MIT EECS Jin-Au Kong Outstanding Doctoral Thesis Prize. Her research interests include Bayesian nonparametrics, Bayesian dynamic modeling and time series analysis. The work emphasizes methodology for high-dimensional, sparsely sampled data with applications in neuroscience, health monitoring, and finance, amongst others.
How Much: Free
How: Register online
