Miaomiao Zhang     
Miaomiao Zhang
Postdoctoral Associate

"Bayesian Models on Manifolds for Image Registration
and Statistical Shape Analysis"

Tuesday, March 29, 4:00 PM
Packard Lab room 466

Abstract:  Investigating clinical hypotheses of diseases and their potential therapeutic implications based on large medical image collections is an important research area in medical imaging. The use of medical images provides clinicians an insight about anatomical changes caused by diseases; hence is critical to disease diagnosis and treatment planning. However, 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. In this talk, I will present efficient, robust, and reliable methods to address these problems. My approach entails creating (i) an efficient image registration approach for deriving anatomical shapes from the large-scale image database, 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.

Bio:  Miaomiao Zhang is a postdoctoral associate in Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. She completed her PhD in the Computer Science Department at University of Utah. Her research work focuses on developing novel models at the intersection of statistics, mathematics, and computer engineering in the field of medical and biological imaging. Miaomiao Zhang received the Young Scientist Award at the 2014 Medical Image Computing and Computer-Assisted Intervention (MICCAI).

© 2014-2016 Computer Science and Engineering, P.C. Rossin College of Engineering & Applied Science, Lehigh University, Bethlehem PA 18015.