Miaomiao Zhang
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CONTACT: Electrical and Computer Engineering Rice Hall 300 Tel: 434-924-6146 |
Join MIA Lab 
Our MIA (Medical Image Analysis) lab is looking for highly motivated graduate research assistants. Prior research experience in image analysis, machine learning, or other related areas is a plus. Programming background in Python or C/C++ is preferred. If you are interested in joining us, please send Dr. Zhang an email along with your CV in advance.
About Me
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 developing machine learning methods in image registration, segmentation, and statistical shape analysis, with applications to cardiovascular imaging, neuroimaging, and computer-assisted surgery. 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 have been actively serving as an area chair for MICCAI, ISBI, and MIDL.
Recent News
February 2024: Our paper on “Multimodal learning to improve cardiac late mechanical activation detection from cine MR images” is accepted by ISBI 2024! Arxiv coming up soon!
February 2024: I am trilled to be one of the Nationally Recognized Award Winners recognized by UVA!
December 2023: Congratulations to our team winning Best Thematic Paper Award at ML4H 2023!
November 2023: Our paper on “Diffusion Models To Predict 3D Late Mechanical Activation From Sparse 2D Cardiac MRIs” was accepted at ML4H 2023!
November 2023: I will be talking about “Deep Shape Analysis For Healthcare” at the JIS Summit 2023! Join the discussion online if you are interested!
October 2023: Our collaborated work with Dr. Fred Epstein on “TransStrainNet: Improved Strain Analysis of Cine MRI with Long-Range Spatiotemporal Relationship Learning” was accepted as an oral at SCMR 2024!
September 2023: I am hosting Dr. Yinzhi Cao at the Charles L. And Ann Lee Brown distinguished seminar series. More information can be found here.
August 2023: Our paper on 'SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction’ was selected as an oral presentation by MICCAI ShapeMI.
June 2023: The video presentation of our work on ’NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces’ is released online!
May 2023: I will be giving a keynote on “Deep Neural Networks To Analyze Deformable Shapes From Images” at MICCAI ShapeMI! Looking forward to seeing you in Vancouver, Canada this October!
April 2023: I am hosting Dr. Polina Golland at the Charles L. And Ann Lee Brown distinguished seminar series. More information can be found here.
April 2023: I was recognized with the UVA ECE Faculty Research Award! Yay!
April 2023: I received NIH Trailblazer R21 Award! Hooray! Looking forward to my next project with the amazing collaborators Dr. Fred Epstein and Dr. Ken Bilchick!
April 2023: Congratulations to my student Jian Wang, who has successfully defended his PhD dissertation today!!! Well done, Dr. Jian Wang!!! Wish you all the best joining Harvard Medical School as a postdoctoral researcher in your next adventure!
April 2023: Our paper on ’NeurEPDiff: Neural Operators to Predict Geodesics in Deformation Spaces’ was selected as an Oral Presentation by IPMI!
April 2023: Our paper on ’MetaMorph: Learning Metamorphic Image Transformation With Appearance Changes’ was selected as a Poster Presentation by IPMI!
March 2023: Our work with Dr. Jundong Li received the UVA Research Innovation Award (RIA)! Yay!
February 2023: Two papers were accepted at IPMI! ArXiv coming up soon!
January 2023: Three papers were accepted by ISBI.
January 2023: I received NSF CAREER Award! Hooray!
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!
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 analysis’ 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 )