The data science research area focuses on developing innovative methods for analyzing large-scale heterogeneous data that assists the process of making complex decisions in a timely manner. These areas include data analytics, production economics, simulation, spatial optimization and stochastic optimal control.
Enhancing the Accuracy of Computer Simulations
Enhancing the accuracy of computer experiments requires new statistical and data science methodologies that can determine how these experiments should be designed, how data from the experiment should be analyzed and how to create more accurate simulations.
Dr. Rui Tuo’s National Science Foundation-funded research aims to establish a new uncertainty quantification method — an approach that seeks to minimize uncertainties in computational experiments — through experimental design, data analysis, and model validation and calibration.
Creating and enhancing computer simulations through the development of new models to measure their efficiency and cost will help improve simulations and impact many areas of research.
Featured Student
Imtiaz Ahmed is a 2020 graduate and works as a postdoctoral scholar at Texas A&M University. His dissertation is titled “Unsupervised Anomaly Detection of High-Dimensional Data with Low-Dimensional Embedded Manifold.” Ahmed also placed first in the 2019 INFORMS Annual Conference Poster Presentation.