Miaomiao Zhang

Investigating clinical hypotheses of diseases and their potential therapeutic implications based on large medical image collections is an important research area in medical imaging. Medical images provide an insight about anatomical changes caused by diseases; hence is critical to disease diagnosis and treatment planning. Characterization of the anatomical changes poses computational and statistical challenges due to the high-dimensional and nonlinear nature of the data, as well as a vast number of unknown model parameters. My current interests lie in developing efficient, robust, and reliable methods to address these problems.

My approach entails (i) developing a low-dimensional shape descriptor to represent anatomical changes in large-scale image data sets, and (ii) novel Bayesian machine learning methods for analyzing the intrinsic variability of high-dimensional manifold-valued data with automatic dimensionality reduction and parameter estimation. The potential practical applications of this work beyond medical imaging include machine learning, computer vision, and computer graphics