Dr. Liu has a solid background in computational neuroscience and machine learning, leading a wide range of research spanning image reconstruction, multimodal data processing, and data analysis. Her primary research interest lies in studying pathological changes in the human brain by creating innovative machine learning technologies for automated brain disease analysis. Current efforts are being directed to extending these works to harmonize/adapt multi-site data, integrate multiple data modalities (e.g., MRI, PET, fMRI, DTI, CSF, clinical, and genetic data), and apply the resulting analysis paradigms to a number of important clinical studies such as Alzheimer’s disease (AD), AD related disorders (ADRD), late-life depression (LLD), and major depressive disorder (MDD) analysis.
Society Membership
- IEEE Senior Member, Institute of Electrical and Electronics Engineers (IEEE) Society, 2019-present
- Member, Organization for Human Brain Mapping (OHBM), 2020-present
- Member, Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, 2014-present
Leadership
- Director, Machine Intelligence in Biomedical Computing (MAGIC) Lab
- Co-Director, Radiological Sciences Faculty Development, Department of Radiology, UNC-Chapel Hill
Academic Award
- Outstanding Area Chair Award, MICCAI, 2023.
- Top 1% Highly Cited Article in Computer Science Paper, Elesvier, 2018.
- Featured Article, IEEE Journal of Biomedical and Health Informatics, 2018.
- Outstanding Doctoral Dissertation/Thesis Nomination Award, Chinese Association for Artificial Intelligence (CAAI), 2016.
- MICCAI Society Young Scientist Award Nomination, 2016.
- MICCAI Travel Award, 2016.
- Outstanding Doctoral Dissertation/Thesis Award, Computer Society of Jiangsu Province, China, 2016.
- Outstanding Doctoral Dissertation/Thesis Award, Nanjing University of Aeronautics and Astronautics, 2016.
- Graduate Student Research and Innovation Program Funding (No. CXZZ13_0173), Jiangsu Province, China, 2013-2014.
- Outstanding Graduate Student Award, Nanjing University of Aeronautics and Astronautics, 2013.
- IAPR Travel Award, The 21st International Conference on Pattern Recognition (ICPR 2012), 2012.
Editorial Appointment
- Editorial Board Member, Medical Image Analysis, 2023-present
- Associate Editor, IEEE Transactions on Cognitive and Developmental Systems, 2023-present
- Associate Editor, Neural Networks, 2023-present
- Associate Editor, Meta-Radiology, 2023-present
- Associate Editor, Pattern Recognition, 2020-present
- Associate Editor, Frontiers in Aging Neuroscience, 2020-present
- Editorial Board Member, Brain Sciences, 2022-present
- Academic Editor, PLOS ONE, 2019-present
- Editorial Board Member, Scientific Reports, 2021-present
- Guest Associate Editor, Frontiers in Brain Imaging Methods, 2021
- Guest Associate Editor, Frontiers in Artificial Intelligence in Radiology, 2021
- Guest Associate Editor, Frontiers in Neuroinformatics, 2021
- Grant Review Panel Member, Swiss National Science Foundation (SNSF), 2021-2023
- Grant Review Panel Member, KU Leuven, University of Leuven, Belgium, 2021
- Grant Review Panel Member, Dutch Research Council, Netherlands Organization for Scientific Research (NWO), 2020
- Grant Review Panel Member, Medical Research Council (MRO), UK Research and Innovation, 2020
- Grant Review Panel Member, Netherlands Organization for Scientific Research (NWO), 2020
- Grant Review Panel Member, Netherlands Organization for Scientific Research (NWO), 2018
- Grant Review Panel Member, Personalized Health and Related Technologies (PHRT) Program of ETH Zürich, 2018
- Guest Editor, Journal of Neuroscience Methods, Special Issue on “Deep Learning Methods and Applications in Neuroimaging”, Dec. 2018
- Guest Editor, Multimedia Tools and Applications, Special Issue on “Multimodal Data Fusion, Learning, and Application”, Feb. 2016
- Guest Editor, Multimedia Systems, Special Issue on “Multimedia Analysis for Medical Applications”, Nov. 2016
- Guest Editor, Neurocomputing, Special Issue on “Multimodal Media Data Understanding and Analytics”, Jan. 2016
Committees
- Area Chair, The 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2023
- Senior Program Committee Member, The 36th AAAI Conference on Artificial Intelligence (AAAI), 2022
- Oral Session Chair, The 13th International Workshop on Machine Learning in Medical Imaging (MLMI), 2022
- Area Chair, The 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021
- Oral Session Chair, The 24th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021
- Oral Session Chair, The 12th International Workshop on Machine Learning in Medical Imaging (MLMI), jointly with MICCAI, 2021
- Oral Session Chair, The 27th IEEE International Conference on Image Processing (ICIP), 2021
- Oral Session Chair, The 18th IEEE International Symposium on Biomedical Imaging (ISBI), 2021
- Chair, The 11th International Workshop on Machine Learning in Medical Imaging (MLMI), jointly with MICCAI, 2020 [link]
- Area Chair, The 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2020
- Oral Session Chair, 2020 IEEE International Conference on Data Mining (ICDM), 2020
- Area Chair, The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019 [link]
- Oral Session Chair, The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019 [link]
- Invited Speaker, Tutorial on “Hypergraph Learning: Methods, Tools and Applications in Medical Image Analysis“, jointly with MICCAI 2019, 2019 [link]
- Co-Chair, The 10th International Workshop on Machine Learning in Medical Imaging (MLMI), jointly with MICCAI, 2019 [link]
- Co-Chair, The 1st International Workshop on Graph Learning in Medical Imaging (GLMI), jointly with MICCAI, 2019 [link]
- Invited Speaker, Vision and Learning Seminar (VALSE) Webinar on “Brain Inspired Visual Model”, 2019 [link]
- Co-Chair, The 9th International Workshop on Machine Learning in Medical Imaging (MLMI), jointly with MICCAI, 2018 [link]
- Program Committee Member, The AAAI Conference on Artificial Intelligence (AAAI), 2017-2021
- Program Committee Member, International Joint Conference on Artificial Intelligence (IJCAI), 2018-2019
- Program Committee Member, The 15th Pacific Rim International Conference on Artificial Intelligence (PRICAI), 2018
- Program Committee Member, International Workshop on Machine Learning in Medical Imaging (MLMI), 2016-2020
- Special Session Chair, The 8th International Conference on Internet Multimedia Computing and Service (ICIMCS 2016), Session on “Signal/Image Processing Techniques for Multi-source Image Analysis”, Aug. 2016
- Regular Referee for IEEE Trans. on Pattern Analysis and Machine Intelligence, Nature Communications, IEEE Trans. on Medical Imaging, Medical Image Analysis, Cerebral Cortex, IEEE Trans. on Cybernetics, IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Biomedical Engineering, IEEE Trans. on Industrial Informatics, NeuroImage, Human Brain Mapping, IEEE Journal of Biomedical and Health Informatics, EBioMedicine, Information Fusion, Pattern Recognition, Applied Intelligence, Neural Networks, IEEE Trans. on Neural Systems and Rehabilitation Engineering, Scientific Reports, PLOS ONE, Neurocomputing, Multimedia Tools and Applications, Multimedia Systems, Frontiers of Computer Science, IEEE Access, Science China Information Sciences, MLMI 2016-2021, MICCAI 2015-2020, AAAI 2017-2021, IJCAI 2018-2019, GLMI 2019
Selected Publications
- M. Liu, D. Zhang, S. Chen, H. Xue. Joint Binary Classifier Learning for ECOC-based Multi-class Classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(11): 2335-2341, 2016.
- M. Liu, D. Zhang, D. Shen. Relationship Induced Multi-template Learning for Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment. IEEE Transactions on Medical Imaging, 35(6): 1463-1474, 2016.
- M. Liu, D. Zhang. Pairwise Constraint-guided Sparse Learning for Feature Selection. IEEE Transactions on Cybernetics, 46(1): 298-310, 2016.
- M. Liu, J. Zhang, P.-T. Yap, D. Shen. View-aligned Hypergraph Learning for Alzheimer’s Disease Diagnosis with Incomplete Multi-modality Data. Medical Image Analysis, 36(2): 123-134, 2017.
- M. Wang, D. Zhang, D. Shen, M. Liu*. Multi-task Exclusive Relationship Learning for Alzheimer’s Disease Progression Prediction with Longitudinal Data. Medical Image Analysis, 53: 111-122, 2019.
- C. Lian#, M. Liu#, J. Zhang, D. Shen. Hierarchical Fully Convolutional Network for Joint Atrophy Localization and Alzheimer’s Disease Diagnosis using Structural MRI. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(4): 880-893, 2020.
- M. Wang, D. Zhang, J. Huang, P.-T. Yap, D. Shen, M. Liu*. Identifying Autism Spectrum Disorder with Multi-Site fMRI via Low-Rank Domain Adaptation. IEEE Transactions on Medical Imaging, 39(3): 644-655, 2020.
- Y. Pan, M. Liu*, Y. Xia, D. Shen. Disease-Image-Specific Learning for Diagnosis-Oriented Neuroimage Synthesis with Incomplete Multi-Modality Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10):6839-6853, 2022.
- Hao Guan, Mingxia Liu*. DomainATM: Domain Adaptation Toolbox for Medical Data Analysis. NeuroImage, 268: 119863, 2023.
- Yue Sun, Limei Wang, Kun Gao, Shihui Ying, Weili Lin, Kathryn L. Humphreys, Gang Li, Sijie Niu, Mingxia Liu*, Li Wang. Self-Supervised Learning with Application for Infant Cerebellum Segmentation and Analysis. Nature Communications, 14: 4717, 2023.
*Corresponding Author; #Co-First Author
