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Research Interests:  Predictive models of time series data.

Assume that you are given a time series and your task is to build a model from the observations of the past that you have available. This problem occurs not only in time series analysis, but also for any autonomous, adaptive agent.  Animals and humans are presumably quite good at this task, which they need to solve for survival.
 
Intuitively, a good model is one that predicts the data one hasn't encountered yet, i.e. the future. Furthermore, if two models acheive the same in terms of prediction, but one of the models is simpler than the other one, we prefer the simpler model.

This intuition can be made precise, using information theory. One can derive and study the class of optimally predictive models, and a process's causal compressibility.

S. Still and J. P. Crutchfield. Structure or Noise? 2007. http://lanl.arxiv.org/abs/0708.0654
S. Still, J. P. Crutchfield and C. J. Ellison. Optimal Causal Inference. 2007. http://lanl.arxiv.org/abs/0708.1580


Student Projects (499, 699) and thesis projects:

Applications:

I am looking for a student who wants to apply these algorithms, for example to earth quake prediction or bioinformatics.

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