Georgia Tech's expertise in advanced computer-based analysis, probability and statistics, numerical algorithms and optimization, machine learning, and human-computer interaction techniques provides a strong foundation to lead this new initiative.
Park specializes in using numerical linear algebra and optimization techniques to develop computer-based algorithms that dramatically reduce the dimension and number of data points in massive data sets. Dimension reduction is essential for efficient processing of high-dimension data sets while removing the noise in the data.
Park is especially interested in developing methods for dimension reduction that exploit prior knowledge in the data sets such as clustered structures and non-negativity. This process is important because it leads to more accurate classification and prediction results.
Alexander Gray, an assistant professor in the Computational Science and Engineering Division of the College of Computing, has experience developing efficient algorithms that allow statistical and machine learning methods to be applied to massive datasets. He employs ideas from computational geometry and computational physics to statistical computations.
"Reducing the computation time for an analysis from hours to seconds makes all the difference, since data analysis is inherently an iterative and interactive process," ex
|Contact: Abby Vogel|
Georgia Institute of Technology Research News