Dajiang Zhu, Ph.D.
Dajiang Zhu is an Associate Professor in the Computer Science and Engineering department at the University of Texas at Arlington.
He obtained B.S degrees in Computer Science from Shanghai Jiaotong University, Shanghai, China in 2001, and a Ph.D. degree in Computer Science from University of Georgia, Athens, Georgia in 2014.
His research is focused on brain imaging, computational neuroscience, and big data solutions for medical data analysis.
Our research in MIND mainly focuses on the discovery of fundamental principles of brain structural and functional architectures and their relationship, via brain imaging, computational modeling and machine learning methods. We are interested in the interaction between Artificial Intelligence (AI) and Human Intelligence (HI): Using Deep Learning to facilitate the analysis and interpretation of brain data; Applying neuroscience knowledge to design more efficient Deep Learning architectures. We also have strong interests in applying the discovered principles, theories and methods to better understand neurodevelopmental, neurodegenerative and psychiatric disorders including Autism, Alzheimer’s disease, and Major Depression, among other brain conditions.
☕ Our paper "Longitudinal Infant Functional Connectivity Prediction via Conditional Intensive Triplet Network" was early accepted by International Conference on Medical Image Computing and Computer-Assisted Intervention, 2022 (MICCAI'22).
☕ Our paper "Predicting brain structural network using functional connectivity" was accepted by Medical image analysis (2022).
☕ Our paper "Deep fusion of brain structure-function in mild cognitive impairment" was accepted by Medical image analysis (2021).
☕ Our paper "Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN based Generative Adversarial Network" was early accepted by International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020 (MICCAI'20).
☕ Our paper "Learning Latent Structure Over Deep Fusion Model Of Mild Cognitive Impairment" was accepted by IEEE International Symposium on Biomedical Imaging, 2020 (ISBI'20).
☕ Our paper "Jointly Analyzing Alzheimer's Disease Related Structure-Function Using Deep Cross-Model Attention Network" was accepted by IEEE International Symposium on Biomedical Imaging, 2020 (ISBI'20) (Oral).
☕ Our paper "A Cascaded Multi-Modality Analysis in Mild Cognitive Impairment" of Lu Zhang was accepted by MICCAI 2019' workshop Machine Learning in Medical Imaging (MLMI'19).
☕ Our paper "Multi-Modal Image Prediction via Spatial Hybrid U-Net" of Akib Zaman was accepted by MICCAI 2019' workshop Multiscale Multimodal Medical Imaging (MMMI'19) (Oral) (Best Paper Award).
☕ I organized MBIA 19’ workshop in Shenzhen.
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