February 7, 2017

1:30 pm / 2:30 pm


Miaomiao Zhang
Postdoctoral Associate
Department of Electrical Engineering and Computer Science
Homepage: http://people.csail.mit.edu/miao86/

Low-dimensional Manifold Models for Image Registration and Bayesian Statistical Shape Analysis

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 thendata, 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 (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.

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 Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2014 and was a runnerup for the same award at MICCAI 2016.