Associate Professor & J. Crayton Pruitt Family Term Fellow

Big data analytics, brain informatics, medical image analysis

B.S., Information Engineering, Zhejiang University, China, 2009
Ph.D., Electrical and Computer Engineering, Cornell University, 2014

Research Summary

Dr. Fang’s research focuses on the convergence of artificial intelligence (AI)/deep learning and the brain. Her research themes center around artificial intelligence (AI)-empowered precision brain health and brain/bio-inspired AI. She focuses on questions such as: How to use machine learning to quantify brain dynamics, early diagnose Alzheimer’s disease through novel imagery, predict individualized treatment outcomes, and design precision intervention, as well as how to leverage neuroscience principals to design the next-generation AI. Her Smart Medical Informatics Learning and Evaluation (SMILE) lab aims to develop innovative brain/neuroscience-inspired medical AI/deep learning models to understand, diagnose and treat brain disorders in big and complex data.

Honors and Awards

• J. Crayton Pruitt Family Term Fellow, Department of Biomedical Engineering, University of Florida, 2023-2026
• UF HWCOE Faculty Award for Innovation, 2022
• UF BME Faculty Research Excellence Award, 2021
• CTSI Pilot Award, Precision Medicine, 2019
• Senior Member, Institute of Electrical and Electronics Engineers, 2019
• Association for Computing Machinery’s Inaugural Future Computing Academy, 2017
• Ralph E. Powe Junior Faculty Enhancement Award, 2016
• Robin Sidhu Memorial Young Scientist Award, 2016
• Society of Brain Mapping and Therapeutics, 2016
• National Science Foundation CISE Research Initiation Initiative Award, 2015
• Best Paper Award at IEEE International Conference on Image Processing, 2010

Selected Publications:

Google Scholar Citations Link

Fang R, Pouyanfar S, Yang Y, Chen SC, Iyengar SS. “Computational health informatics in the big data age: a survey,” ACM Computing Surveys (CSUR), 2016 Jun 14;49(1):12.

Fang R, Zhang S, Chen T, Sanelli PC. “Robust low-dose CT perfusion deconvolution via tensor total-variation regularization,” IEEE transactions on medical imaging, 2015 Jul;34(7):1533-48.

Fang R, Chen T, Sanelli PC. “Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning,” Medical image analysis, 2013 May 31;17(4):417-28.

Fang R, Tang KD, Snavely N, Chen T. “Towards computational models of kinship verification,” InImage Processing (ICIP), 17th IEEE International Conference, Sept 26, 2010 (pp. 1577-1580). IEEE.