Abstract
Due to the presence of enormous corpuses of data sets, the dominant scientific paradigm is changing from a hypothesis-driven collection of data to a data-mining-driven exploration of data. However, as data sets continue to grow, two fundamental challenges arise:

1. How do you summarize an enormous data corpus to a size manageable for deeper analysis?

2. How do you bound the error inherent in the data or introduced in the summarization phase?

I will provide fundamental techniques and analysis tools to deal with both of these questions, focusing in this talk on the broad class of data sets that can be interpreted as distributions. Specifically, I will show how to build a variety of distributions for statistics on uncertain data, how to analyze the approximations these techniques admit, and how to scale these techniques up to massive distributed data sets.

BIO
Jeff Phillips has a BS in Computer Science and BA in Math from Rice University and a Ph.D. in Computer Science from Duke University where he was supported by an NSF Graduate Research Fellowship. He is currently a NSF-CCC-CRA Computing Innovations Postdoctoral Fellow at the University of Utah. His interests span the areas of algorithms, data mining, machine learning, databases, and computational statistics, specifically those areas relevant to processing and understanding uncertainty in large scientific data sets.