Personnel

Current Members

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  Yong Fan, PhD

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  Yong Fan, PhD

   Profile
  

  Dr. Fan is a tenured Associate Professor in the Department of Radiology at the University of Pennsylvania.  He has a broad background in medical image analysis and pattern recognition, with specific training in applied mathematics, statistics, and machine learning.

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  Hongming Li, PhD

  Senior Research Investigator
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  Hongming Li, PhD

  Senior Research Investigator
   Profile
  

  Dr. Li is a Senior Research Investigator in the Department of Radiology at the University of Pennsylvania. His research interest focuses on the development of computational algorithms for medical image processing and their application in studies of brain images and cancer images.

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  Yuxin Gong

  Data Analyst
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  Yuxin Gong

  Data Analyst
   Yuxin.Gong@pennmedicine.upenn.edu
  

  Yuxin has a  background related medical image analysis during.  His research interests include semi-supervised fetal heart disease ultrasound image classification, ultrasound image segmentation, brain CT image registration.  Currently, he is working on data harmonization.

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  Liang Ma, PhD

  Postdoc Fellow
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  Liang Ma, PhD

  Postdoc Fellow
   liang.ma@pennmedicine.upenn.edu
  

  Dr. Ma graduated at the School of Artificial Intelligence of the University of Chinese Academy of Sciences. He started his researches in the field of brain science since 2018 at the Brainnetome Center, Institute of Automation, Chinese Academy of Sciences. His current research focuses on AI methods for medical image analysis, individualized biomarkers for mental illnesses, and imaged-guided personalized precision treatments.

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  Jiong Wu, PhD

  Postdoc Fellow
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  Jiong Wu, PhD

  Postdoc Fellow
   jiong.wu@pennmedicine.upenn.edu
  

  Dr. Wu is currently working on two research projects:

  1) Medical image registration. I first focus on the unsupervised learning-based diffeomorphic medical image registration method. To improve the performance of the current multi-resolution registration frameworks, I proposed an inverse consistency strategy that calculates the final velocity field by using the forward and inverse registration velocity fields. In addition, a novel regularization is proposed to focus on the consistency of displacements at different time points in the integration flow path. Finally, a diffeomorphic registration framework utilizing inverse consistency based on four different resolutions is constructed with better performance than state-of-the-art methods

  2) Cortical thickness estimation. Traditional voxel-based cortical thickness estimation methods are time-consuming and their accuracy is not comparable with the FreeSurfer. Then, I try to utilize deep learning-based techniques to mitigate these two main issues. A deep learning-based segmentation model and a diffeomorphic registration model are proposed to integrate into the cortical thickness estimation framework resulting in higher accuracy and efficiency.

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  Yuncong Ma, PhD

  Postdoc Fellow
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  Yuncong Ma, PhD

  Postdoc Fellow
   Yuncong.Ma@pennmedicine.upenn.edu
  

  Dr. Ma has an extensive background related to fMRI during his PhD, primarily focusing on animal neuroimaging techniques including awake rodent fMRI setup, multi-echo fMRI processing, chemogenetics, Calcium fiber photometry, electrophysiology, combined with drug-induced anesthesia to study brain’s dynamics and underlying neurovascular mechanisms. He was involved in sequence design and image reconstruction for zero-echo-time fMRI before joining Penn in July 2022. Currently, he is developing a toolbox for personalized functional network modeling, and joint identification of functionally connected cortical-surface and deep-brain structures for TMS treatment.

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  Yuemeng Li

  PhD Student
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  Yuemeng Li

  PhD Student
   Yuemeng.Li@seas.upenn.edu
  

  Yuemeng is a PhD candidate, majoring in bioengineering in the field of medical imaging analysis. With a strong interest in the application of machine learning techniques to the field of medical imaging, her research focused on lung nodule detection, brain structural segmentation, accelerated diffusion-weighted MRI reconstruction and urinary tract segmentation. Her work aims to develop efficient and accurate advanced machine-learning algorithms that can assist clinical treatment decisions, leading to better outcomes for patients.

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  Xiaoyang Chen, PhD

  Postdoc Fellow
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  Xiaoyang Chen, PhD

  Postdoc Fellow
   Xiaoyang.Chen@pennmedicine.upenn.edu
  

  Dr. Chen has expertise encompassing diverse medical image analysis tasks, such as medical image segmentation, object/anatomical landmark detection, disease classification and brain morphometry. His research currently focuses on developing foundation models for multi-modality multi-organ segmentation with the ultimate objective of practical implementation in clinical settings. The primary goal of this research is to facilitate personalized treatment of patients and enhance clinical outcomes.

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  Hao Zheng, PhD

  Postdoc Fellow
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  Hao Zheng, PhD

  Postdoc Fellow
   Hao.Zheng@pennmedicine.upenn.edu
  

  Dr. Zheng's research fields are computer vision, deep learning, and data visualization. He has focused on the intersection of medical image analysis and artificial intelligence, especially in designing data-efficient learning methods and improving model generalization on multi-domain/site data. He has extensive experience on segmentation, classification, geometric learning, and scientific data visualization. Currently, he is working on surface-based cortical analysis from brain MRIs and multi-organ segmentation from CTs, which aims to assist and accelerate disease diagnosis and scientific discovery.

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  Tingting Chen, PhD

  Postdoc Fellow
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  Tingting Chen, PhD

  Postdoc Fellow
   tingting.chen@pennmedicine.upenn.edu
  

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  Peixian Liang, PhD

  Postdoc Fellow
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  Peixian Liang, PhD

  Postdoc Fellow
   Peixian.Liang@pennmedicine.upenn.edu
  

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  Ran Gu, PhD

  Postdoc Fellow
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  Ran Gu, PhD

  Postdoc Fellow
   ran.gu@pennmedicine.upenn.edu
  

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