Assistant Professor of the Department of Radiology and Biomedical Research Imaging Center (BRIC) at the University of North Carolina at Chapel Hill. Her current efforts are being directed at standardizing and integrating data across sites and modalities to advance studies in Alzheimer’s disease, its related disorders, and late-life depression. An IEEE Senior Member since 2019, Liu has played significant roles in academic conferences and journals, including as an Area Chair at major events like MICCAI and AAAI. She is severing as an Associate Editor in several journals such as Medical Image Analysis, Pattern Recognition, and Neural Networks. She also served as Area Chair, Session Chair and Senior Program Committed Member at MICCAI, AAAI, and ICDM. Her lab has made available a suite of open-source computational tools for analyzing biomedical data, such as Domain Adaptation Toolbox for Medical Data Analysis and the Augmentation and Computation Toolbox for Brain Network Analysis. 

Associate Professor of the Department of Radiology and Biomedical Research Imaging Center (BRIC) at the University of North Carolina at Chapel Hill. His research focuses on the development and dissemination of innovative computational and artificial intelligence techniques for neuroimaging analysis, with 250+ peer-reviewed papers published in the field, e.g., PNAS, Nature Protocols, Nature Communications, Medical Image Analysis, IEEE Trans. Medical Imaging, etc. His lab has released a set of widely used computational tools, models, and atlases for neuroimaging analysis, e.g., Spherical U-Net models, 4D Infant Cortical Surface Atlases, 4D Infant Brain Volumetric Atlases, and iBEAT V2.0. He is a Distinguished Investigator of the Academy for Radiology & Biomedical Imaging Research. He has received multiple NIH R01 grant awards as the Principal Investigator. 

Mengqi Wu is a Ph.D. candidate in Biomedical Engineering at the University of North Carolina at Chapel Hill. His research focuses on developing deep learning models for multi-site brain MRI harmonization and multi-modal medical image synthesis. With expertise in neuroimaging processing and analysis, he has worked extensively with MRI and PET with multiple tracers in studies related to Alzheimer's disease and HIV. His work has been published in many peer-reviewed conferences and journals, including MICCAI, ISMRM, and Neural Networks. He also serves as a reviewer for MICCAI, Neural Networks, Medical Image Analysis, and IEEE TCDS.

Jiale Cheng is a Ph.D. candidate in Biomedical Engineering at the University of North Carolina at Chapel Hill, specializing in deep learning and neuroimaging. His research focuses on developing AI-driven methodologies for cortical surface analysis, early brain development modeling, and multi-institutional neuroimaging harmonization. His work aims to improve the reliability and generalizability of machine learning models in medical imaging, particularly for large-scale neurodevelopmental studies. He has published in leading journals and presented at MICCAI, ISBI, ISMRM, and IEEE TMI. He also serves as a reviewer for top-tier conferences and journals in medical imaging and artificial intelligence.