This talk will highlight some of the benefits and challenges associated with harnessing the temporal structure present in many datasets. It will 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. Nonparametric and hierarchical models will be exploited 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 this will be demonstrated by touching upon Dr. Fox’s 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.


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 to 2011 she was a postdoc in the Duke Statistical Science Department. She received her SB, MEng, EE and PhD in Electrical Engineering & Computer Science (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 econometrics, among others.