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
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.
David Kepplinger: Expertise in High-dimensional and Big Data. Statistical Computing, Visualization Analytics, Data Mining, and Data Compression.
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. Research in algorithms, statistical inference, and probability theory.
Abolfazl Safikhani: Expertise in high-dimensional modeling, spatio-temporal data analysis, network modeling with applications in urban analytics, neuroscience, smart cities.
Martin Slawski develops novel statistical machine learning approaches to tackle challenges associated with the processing and analysis of massive data. Data reduction and compression and data integration.
Jiayang Sun: Expertise in Longitudinal/spatial data, big data, Causal Inference, Heterogeneous population, Survey/Study Design/Clinical Trial, EHR, NLP, ML/Ai, Imaging, Bioinformatics.
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.
Lily Wang: Expertise in High-dimensional and Big Data. Statistical Computing, Visualization Analytics, and Data Mining. Machine Learning (ML), Artificial Intelligence (AI), and Interface of Statistics and Computer Science. Data Compression.