National Science Foundation and Georgia Tech
This talk will have two components. In the first part, I will give an overview of NSF, and its resources that are relevant to the statistical science. Some related programs that are beyond the Division of Mathematical Science, such as the Big Data program and the Computational and Data-enabled Science and Engineering program, will be reviewed. Some suggestions regarding how to apply to these programs may be provided.
In the second part of this talk, I will describe some of my own recent research. One of them is about the statistical dependence. Distance correlation had been introduced as a better alternative to the celebrated Pearson’s correlation. The existing algorithm for the distance correlation may require an O(n^2) algorithm, and I will show how it can be done in O(n log n). If time permits, I will also discuss a new exact recovery condition that we obtained regarding feature extraction via the Nonnegative Matrix Factorization.
More information about Xiaoming Huo may be found at: