Our researchers are nationally recognized experts in data analytics. Their work includes investigating statistical problems arising in privacy and security analytics, the statistical analysis of literary style, and the statistical foundations of geometric and topological data analysis.
Here’s a look at our faculty’s expertise
Pramita Bagchi works on the analysis of functional data under dependence, specifically in time series and spatial context. Modeling high-resolution or high-frequency observations as functional data has gained significant attention in the recent past. Her research focuses on model validation and inference for such data with potential applications in finance, environmental science, geology, and neuroscience.
Daniel Carr's research includes exploratory visualization of moderate-dimensional aggregated data summaries. For example, his work has addressed the use of NASA's cluster-compressed summaries of global multivariate multi-altitude data from the Atmospheric Infrared Sounder. The clusters-compressed summaries were aggregated in geospatial regions. The distance between a pair of regions was computed using an earth mover's distance as applied to the constituent cluster-compressed summaries. The resulting large distance matrix supported clustering and visualization of geospatial region clusters on the global for chosen time periods.
David Holmes applies stylometry, a statistical analysis of literary style, to investigate the authors of famous literary and historical documents. He has studied the Rights of Man written by Thomas Paine, a leading political philosopher and writer during the American Revolution; The Heart of a Soldier: Intimate Wartime Letters from General George E. Pickett C.S.A. to His Wife by LaSalle Corbell Pickett; and The Expert at the Card Table: The Classic Treatise on Card Manipulation by S. W. Erdnase.
Wanli Qiao researches data in the form of point clouds embedded in Euclidean space, which are numerous in many scientific fields such as geoscience, astronomy, and neuroscience. The geometric features of the system that produces the data are particularly interesting to many researchers. In-depth study of their estimation raises challenges in modern statistics and machine learning. His research interests involve algorithms, statistical inference and probability theory related to these geometric features.
Martin Slawski develops novel statistical machine learning approaches to tackle challenges associated with the processing and analysis of massive data. His recent work has focused on computationally efficient techniques for data reduction and compression, as well as on aspects of record linkage and data integration. His research combines a solid foundation in statistics with expertise in other areas in mathematics, computer science, and engineering. He also has a strong record of interdisciplinary collaborations in the life sciences including genomics, neuroscience, proteomics, and epigenetics.
Anand Vidyashankar focuses on statistical problems arising in privacy and security analytics. He is collaborating with scientists from McKesson Corporation to identify sources of risk and statistical methods to measure and mitigate risk in real-time environments. The work involves integrating aspects of regularity guidelines with novel statistical methods in ultra-high dimensions to develop next-generation privacy and security guidelines.