I'm a data scientist in the Imaging Research Center, Department of Radiology at Cincinnati Children's. My research area focuses on artificial intelligence (AI) technologies in medical imaging. I have applied my expertise to various disease conditions, including neurodevelopment deficits in newborn infants, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder and liver diseases.
During my education to obtain my PhD, I had a chance to work as a part-time research assistant in the Department of Radiology at Cincinnati Children's. As I learned more about medical images, I realized that it would be more meaningful to apply my computer science expertise to the healthcare field. This decision led to my career in medical imaging. My goal is to facilitate the clinical translation of AI technologies to improve the healthcare quality and safety of pediatric patients nationally and globally.
I develop advanced machine learning and deep learning approaches for medical images to aid radiologists and pediatricians in various clinical applications. I also created a novel transfer learning approach for deep learning models to understand human brain networks better. I proposed the first deep learning model to stratify the severity of liver stiffening using T2-weighted abdominal magnetic resonance images (MRI).
Specifically, my research experience includes image reconstruction and denoising, image segmentation, image biomarker identification, image-based disease diagnosis, prognosis and clinical outcome predictions.
I have been a researcher for over 15 years and began my career at Cincinnati Children's in 2013. I am honored to have received the Walter E. Berdon Award for best clinical research paper from the Pediatric Radiology journal (2021). Our team's deep learning study on ADHD was the featured publication by the Radiological Society of North America (2019).
When I'm not working, I enjoy reading.
BS: Electrical Engineering, Northeastern University, Liaoning, China, 2004.
MS: Electrical Engineering, Northeastern University, Liaoning, China, 2007.
PhD: Computer Science and Engineering, University of Cincinnati, OH, 2013.
Post-Doc: Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 2016.
Liver diseases; neonatology
Machine learning; deep learning; medical image analysis
Radiology
Supervised contrastive learning enhances graph convolutional networks for predicting neurodevelopmental deficits in very preterm infants using brain structural connectome. Neuroimage. 2024; 291:120579.
A systematic review of automated methods to perform white matter tract segmentation. Frontiers in Neuroscience. 2024; 18:1376570.
HIV-1 mRNA knockdown with CRISPR/CAS9 enhances neurocognitive function. Journal of NeuroVirology. 2024; 30:71-85.
Machine Learning Diagnosis of Small-Bowel Crohn Disease Using T2-Weighted MRI Radiomic and Clinical Data. American Journal of Roentgenology. 2024; 222:e2329812.
SARS-CoV-2 RNA persists in the central nervous system of non-human primates despite clinical recovery. Molecular Biomedicine. 2023; 4:39.
A novel collaborative self-supervised learning method for radiomic data. Neuroimage. 2023; 277:120229.
Dynamic weighted hypergraph convolutional network for brain functional connectome analysis. Medical Image Analysis. 2023; 87:102828.
Recent Advances in Explainable Artificial Intelligence for Magnetic Resonance Imaging. Diagnostics. 2023; 13:1571.
A Semi-Supervised Graph Convolutional Network for Early Prediction of Motor Abnormalities in Very Preterm Infants. Diagnostics. 2023; 13:1508.
PREDICTION OF FONTAN OUTCOMES USING T2-WEIGHTED MRI RADIOMIC FEATURES AND MACHINE LEARNING. Journal of the American College of Cardiology. 2023; 81:1618.