Miaomiao Zhang

Miaomiao Zhang 

Contact:

Electrical and Computer Engineering
Computer Science

Rice Hall 300
85 Engineer's Way
Charlottesville, VA 22903

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 2018-19, 2022.

Recent News

October 2022: I am excited to give a guest lecture on Deep Learning in Image Registration at RISE-AFRICAI - the first VIRTUAL and FREE Winter School in “AI in Medical Imaging” to empower minorities in low-middle income countries (LMIC)! Join us if interested!

October 2022: I will be serving as a judge in The Annual Biomedical Research Conference for Minoritized Scientists (ABRCMS). Please feel free to reach out to me if you are interested in UVA Engineering program!

September 2022: Our paper on 'Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers’ was accepted by NeurIPS 2022! ArXiv coming up soon!

August 2022: My 8-month old and I participated our first infant-mother regulation study in the Developmental Neuroanalytics Lab in the UVA Department of Neurology!

May 2022: Welcome a new PhD student Tonmoy Hossian to our group!

April 2022: I am hosting Dr. Le Lu at the Charles L. And Ann Lee Brown distinguished seminar series. More information can be found here.

February 2022: I am co-organizing Shape Uncertainty Quantification Meets Shape Statistics at the SIAM Conference on Uncertainty Quantification 2022! Looking forward to (e-)meeting you on April 15th!

January 2022: Our paper on 'Hybrid Atlas Building with Deep Registration Priors’ was accepted by ISBI.

December 2021: I am a program chair for an upcoming workshop on Biomedical Image Registration 2022! Check out the program here and we look forward to your submissions!

October 2021: Our paper on 'Deep Learning for Regularization Prediction in Diffeomorphic Image Registration’ was accepted by the Journal of Machine Learning for Biomedical Imaging (MELBA).

October 2021: I am hosting Dr. Baba C. Vemuri at the Charles L. And Ann Lee Brown distinguished seminar series. More information can be found here.

September 2021: I am hosting Dr. Shuo Li, who is the first Charles L. And Ann Lee Brown distinguished seminar speaker at UVA 2021 Fall. More information can be found here.

July 2021: Our paper on 'Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods: Application to Retinal Diagnostics’ was selected as an oral presentation at MICCAI-PPML.

June 2021: I will be giving a keynote talk on 'Deep Networks for predictive image registration and statistical shape analkysis’ at CVPR workshop DiffCVML.

June 2021: Congratulations to my PhD student Jian Wang who received MICCAI student travel award 2021!

June 2021: Our paper on 'Bayesian Atlas Building with Hierarchical Priors for Subject-specific Regularization’ was accepted by MICCAI.

February 2021: Our research on 'Multi-task Deep Learning for Late-activation Detection of Left Ventricular Myocardium’ was selected as an oral presentation at ISMRM.

January 2021: Our paper on 'Deep Networks To Automatically Detect Late-Activating Regions Of The Heart’ was accepted by ISBI.

February 2020: Our paper on 'DeepFLASH: An Efficient Network for Learning-based Medical Image Registration’ was accepted by CVPR.

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)

DeepFLASH (a Python library for deep learning based fast LDDMM image registration)

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

HierarchicalBayesianAtlasBuilding (a Python library for Bayesian atlas building with estimated regularization )