XIAOMING HUO 
School of Industrial and Systems Engineering
Georgia Institute of Technology and
National Science Foundation
“Fast Computing for Statistical Dependency”
ABSTRACT
At the beginning, I will give an overview of my responsibilities at the National Science Foundation. In particular, I will focus on those that are related to the statistical science. In the remaining (major) part of this talk, I will describe my own recent research: computing for statistical dependence. Distance correlation had been introduced as a better alternative to the celebrated Pearson’s correlation. The existing algorithm for the distance correlation seemingly requires an O(n^2) algorithm, and I will show how it can be done in O(n log n). Moreover, many other statistical dependency related quantities can be computed efficiently. I will give some other examples. Part of the second component is based on a joint work with my NSF colleague, Dr. Gabor Szekely.
