Member

Dr. Gang Li is an Assistant Professor in the University of North Carolina at Chapel Hill. He completed his PhD in 2010 from Northwestern Polytechnical University. He was a Research Fellow at The Methodist Hospital Research Institute, Weill Medical College of Cornell University (2007-2008) and a Research Assistant at Harvard Medical School (2005-2007).

Dr. Zhengwang Wu is a postdoc researcher at the University of North Carolina at Chapel Hill. He got his PhD degree on pattern recognition and intelligent system from Xi’an Jiaotong University, China. His research focuses on computer vision, machine learning, pattern recognition methods and their applications on medical image segmentation, classification and visualization. He is currently working on infant cortical surface atlas construction and computational pipeline development, as well as using state-of-the-art machine learning methods for infant MRI data analysis.

Dr. Fan Wang is a post-doc researcher with the University of North Carolina at Chapel Hill. She has received her Ph.D. in computer science from INSA de Rouen, France in 2016. Her current research focuses on building the postnatal brain parcellation map based on functional and/or structural information.

Dr. Dan Hu is a post-doc researcher with the University of North Carolina at Chapel Hill. She has received her Ph.D. in applied mathematics from Beijing Normal University in 2005. Her current research focuses on the study of infant brain development based on functional and/or structural information.

Dr. Liangjun Chen is a postdoc researcher at the University of North Carolina at Chapel Hill. He has received his Ph.D. in computer science from Xi’an Jiaotong University, China. His research focuses on machine learning and their applications on medical image segmentation, classification, and visualization. He is currently working on using state-of-the-art machine learning methods for intensity nonuniformity correction of infant brain MR images.

Dr. Xin Zhang is an Associate Professor in School of Electronics and Information, South China University of Technology. She completed her Ph.D. degree in 2011 from Oklahoma State University, U.S. Her research focuses on computer vision, machine learning and their applications on non-rigid object motion estimation and recognition. Her major research topic here is deep learning based infant brain development analysis.

Fenqiang Zhao is a Ph.D. candidate at the College of Biomedical Engineering & Instrument Science, Zhejiang University in China. He mainly works on surface mesh-based deep learning methods for analyzing infant cortical surface data.

Lufan Liao is currently a master student at School of Electronics and Information, South China University of Technology. He is now working on MRI-based deep learning methods for fetal age prediction.

Ying Huang is a Ph.D. candidate at the School of Automation, Northwestern Polytechnical University in China. Her current research focuses on the analysis of infant brain development based on functional and/or structural information.

Tao Zhong is a Ph.D candidate at the School of Biomedical Engineering, Southern Medical University. His work mainly focus on deep learning based fetal and infant brain image segmentation.

Yuchen Pei is a Ph.D. candidate from the School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University. Her current research focuses on fetal brain reconstruction based on deep learning.