Miaomiao Zhang

Miaomiao Zhang 

Contact:

Electrical and Computer Engineering
Computer Science

Thornton Hall, E203
351 McCormick Road
Charlottesville, VA 22904

Tel: 434-924-6146
Email: mz8rr AT virginia DOT edu

Join Us

I am looking for highly motivated graduate research assistants. Prior research experience in image / shape analysis, machine learning, or other related areas is a plus. Programming background in C/C or Python is preferred. If you are interested in joining our lab, please send me an email along with your CV in advance.

Biography

I am an assistant professor in the Electrical and Computer Engineering and Computer Science at University of Virginia. My research work focuses on developing novel models at the intersection of statistics, mathematics, and computer engineering in the field of medical and biological imaging. More specifically, my current research projects include image registration/segmentation, statistical shape analysis to quantify anatomical changes, and developing machine learning methods with applications to neuroimaging and computer-assisted neurosurgery. Before joining UVA, I completed my Ph.D. in Computer Science at University of Utah and worked as a postdoctoral associate at Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. I received the Young Scientist Award 2014 and was a runnerup for Young Scientist Award 2016. I am a member of MICCAI society and an area chair for MICCAI 2018, 2019.

Recent News

August 2019: Our paper on 'Plug-and-Play Priors for Reconstruction-based Placental Image Registration’ was accepted by MICCAI workshop PIPPI.

August 2019: Our paper on 'Mixture Probabilistic Principal Geodesic Analysis (MPPGA)’ was accepted by MICCAI workshop MFCA.

July 2019: Our paper on 'On the Applicability of Registration Uncertainty’ was selected as an oral presentation at MICCAI.

June 2019: Congratulations to my PhD student Jian Wang who won the BEST POSTER AWARD at IPMI!

April 2019: Our paper on 'Registration Uncertainty Quantification Via Low-dimensional Characterization of Geometric Deformations’ was published at the Journal of Magnetic Resonance Imaging.

Feb 2019: Our paper on 'Data-driven Model Order Reduction For Diffeomorphic Image Registration’ was accepted at IPMI.

Public Released Softwares

FLASH (a free C++ library of fast diffeomorphic image registration)

https://bitbucket.org/vakra/manifoldstatistics (a C++ library for probabilistic principal geodesic analysis is released and integrated in manifold statistics package)