I am a computer scientist with expertise in artificial intelligence and medical imaging. My long-standing career goal is to develop and validate robust clinically-effective AI diagnostic/ prognostic systems for physicians to use to improve diagnosis/ prediction and prevention of patient outcomes for high-risk infants and children. I have led multiple NIH- and institution- funded studies to develop MRI prognostic biomarkers and deep learning models for early detection/ prediction of various important clinical outcomes, including cognitive, language, and motor deficits, attention deficit hyperactivity disorder, autism spectrum disorder, and chronic liver diseases. I am driving the clinical translation and implementation of AI technologies in context of improved value of medical imaging (improved outcomes associated with a lowering of healthcare costs).

After receiving my master’s degree in Electrical and Computer Engineering from the University of Dayton in 2016 and Ph. D. degree at the University of Dayton in 2021, I worked in the He Lab as a postdoctoral research fellow since 2021. My research interests include medical image processing, deep learning, machine learning, computer vision, video restoration, and enhancement.

I work as a data analyst at Imaging Research Center. I majored in Biomedical Engineering and now I am focusing on processing and analyzing clinical data such as medical image using machine learning and deep learning methods with Python.

At the Imaging Research Center, I work as a research assistant while pursuing my Ph.D. degree in Biostatistics at the University of Cincinnati, OH. I am interested in methodology of deep learning, medical image analysis and predictive modeling, and I am currently focusing on and leading the data homogenization work for the group’s LiverR01 project.

I am currently a Research Fellow at He Lab, Imaging Research Center. I received my PhD degree in Computer Science from the University of Georgia in 2022, and master degree in Artificial Intelligence in 2017. My research interests lie in developing computational methodologies for image understanding problems in the fields of medical image analysis and computer vision. I try to solve medical imaging problems using deep learning-based methods. I am also excited in interdisciplinary research, collaborating, and working with researchers from related fields.

I am a computer scientist focusing on applying machine learning algorithms to medical image analysis. Specifically, using advanced machine learning tools, such as TensorFlow, Keras, and Python, I apply novel deep learning models such as deep neural networks (DNN), deep convolutional neural networks (CNN), U-Net, and graph convolutional networks (GCN) to diagnosis and prognosis of neurodevelopment, Autism, ADHD, as well as liver fibrosis.

I am currently a Ph.D. student in Computer Science at the University of Cincinnati and Graduate research assistant at Imaging Research Center, Cincinnati Children’s Hospital Medical Center. My research area focus on probabilistic representation learning, contrastive and metric learning, and self-supervised learning, and my research interests are Deep learning; Probabilistic reasoning; Self-supervised learning; Representation learning; Medical imaging analysis.

I am a data scientist at the Imaging Research Center in the Cincinnati Children’s Hospital Medical Center. My background is in machine learning, life science, and bioinformatics applications. I focused on designing, developing, and optimizing algorithms used in medical image research. I am skilled in Machine Learning Toolkits (Python, Pytorch, Transformer, scikit-learn) and other data science and coding platforms (Java. R, LAMP Stack).

I am Jinzhao Qian, a PhD graduate student in University of Cincinnati. I work as a research assistant in CCHMC, mainly on algorithm designing and coding. My research interests are computer vision and deep learning. I hope I can help the team to solve problems on medical images with my talent.

Working as a postdoctoral research fellow in the lab, my research focuses on developing graph-based approaches for medical image analysis: using graph topological features, graph signal processing, and graph deep learning models to study mental disorders (schizophrenia, ADHD, bipolar disorder) and brain development. My current studies include disease prediction and biomarker detection with hypergraph learning based deep learning models.

I work as a data scientist at the Imaging Research Center in the Cincinnati Children’s Hospital Medical Center. I am skilled in programming (Python, C++, Cuda, PyTorch and Tensorflow, etc.) and my research interests lies in deep learning, computer vision, intersection between natural language processing and computer vision, medical image analysis.