Dr. Xingfu Wu, associate research professor in the Computer Science and Engineering Division of the Texas A&M Engineering Experiment Station and the Texas A&M University Department of Computer Science and Engineering, has been awarded a three-year grant from the National Science Foundation (NSF).
He is the co-principal investigator with Dr. Faming Liang from the Texas A&M Department of Statistics on the collaborative project, " Efficient Parallel Iterative Monte Carlo Methods for Statistical Analysis of Big Data."
Wu stated in his grant abstract that he and Liang "propose a general principle for developing Monte Carlo algorithms that are feasible for big data and workable on parallel and distributed architectures; that is, using Monte Carlo averages calculated in parallel from subsamples to approximate the quantities that originally need to calculate from the full dataset. This principle avoids the requirement for repeated scans of full data in algorithm iterations, while enabling the algorithm to produce statistically sensible solutions to the problem under consideration.
"The project will have also significant impacts on education through direct involvement of graduate students in the project and incorporation of results into undergraduate and graduate courses. In addition, the package Distributed Iterative Statistical Computing (DISC) that will be developed under this project is designed to provide a platform for Ph.D. students and researchers like the investigators with network-connected computers to experiment new ideas of developing efficient iterative Monte Carlo algorithms in parallel or, more exactly, grid computing environments."
Wu has worked with Dr. Valerie Taylor’s research group, Prophesy, since joining Texas A&M Engineering in 2003. His research interests are performance evaluation and modeling, parallel and cloud computing, scientific computing, parallel programming, and power and energy modeling and analysis in HPC systems.
He is a senior ACM member. His monograph, "Performance Evaluation, Prediction and Visualization of Parallel Systems," was published by Kluwer Academic Publishers (ISBN 0-7923-8462-8) in 1999. He won the best paper award in the 14th IEEE International Conference on Computational Science and Engineering in 2011. To date he has been author or co-author on more than 60 papers in his field.
Wu received his Ph.D. degree from Beijing University of Aeronautics & Astronautics in 1997.