## Special Colloquium

### Monday, January 21st, 2019

**Time:** 4:30 p.m. **Place:** Jeffery Hall 234

**Speaker:** Michelle Miranda (University of Texas)

**Title:** Modeling Modern Data Objects: Statistical Methods for Ultra-high Dimensionality and Intricate Correlation Structures.

**Abstract: ** Advances in technology have been generating data with increased complexity. Modern data objects are often high-dimensional and can also lay in 2D, 3D and even 4D Euclidean and sometimes non-Euclidean planes. One example are data generated from large-scale multi-site studies such as the Human Connectome Project and the Alzheimer's Disease Neuroimaging Initiative. In these scenarios, complexity comes in the form of brain images such as functional magnetic resonance imaging (fMRI), a 4D object that is high-dimensional (number of voxels is around 100K for each time point and each subject), with measurements that are correlated both in time and in space. Another example are data coming from an instrument specifically designed to measure scleral strain in donor eyes. The device generates functional data with functions defined on a non-Euclidean 2D partial spherical domain. Associating these complex data with clinical, environmental, and genetic variables can be challenging and classical statistical tools need to evolve to keep up with the complexity of these new data types. In this talk I address some of the challenges brought by modern data objects and show a few solutions to some important statistical questions in this context.

**Michelle Miranda** obtained her Ph.D. degree at the University of North Carolina at Chapel Hill in 2014. She is currently a Postdoctoral Fellow in Biostatistics at The University of Texas MD Anderson Cancer Center. Her research interests include Bayesian analysis with focus on high-dimensional settings, functional data analysis, and methods for correlated data objects.