Ruogu Fang Portrait

Ruogu Fang

Assistant Professor

Biography

Dr. Ruogu Fang is an Assistant Professor of the School of Computing and Information Sciences at Florida International University in Miami, FL.

Dr. Fang received her Ph.D. degree in Electrical and Computer Engineering from Cornell University in 2014 working under Tsuhan Chen, and Bachelor’s degree from Zhejiang University with the highest honor in 2009. Dr. Fang’s research interests focus on big medical data, brain dynamics, health informatics, machine learning and data mining. She is the recipient of numerous grants, honors and awards, including NSF CRII (pre-CAREER) award as PI, ORAU’s Ralph Lowe Young Faculty Enhancement Award, Robin Sidhu Memorial Young Scientist Award from Society of Brain Mapping and Therapeutics, Best Paper Award at IEEE International Conference on Image Processing, Hottest Paper in Medical Image Analysis, Hsien Wu and Daisy Yen Wu Memorial Award and Irwin and Joan Jacobs Fellowship, to name a few. She has published over 30 peer-reviewed articles, including flagship journals such as IEEE Transaction on Medical Imaging, Medical Image Analysis, ACM Computing Survey, etc. She served as the Co-Chair of the International Workshop on Sparsity Techniques in Medical Imaging, and the Guest Editor of the Journal Computerized Medical Imaging and Graphics. Prof. Fang’s Smart Medical Informatics Learning and Evaluation (SMILE) Lab aims to explore intelligent approaches to bridge the data and medical informatics in the era of big medical data.

Honors and Awards

2016 NSF CRII (pre-CAREER) award as PI
2016 ORAU’s Ralph Lowe Young Faculty Enhancement Award
2016 Robin Sidhu Memorial Young Scientist Award, Society of Brain Mapping and Therapeutics
2014 Hsien Wu and Daisy Yen Wu Memorial Award
2013 Top Hottest Paper, Journal of Medical Image Analysis
2010 Best Paper Award, IEEE International Conference on Image Processing
2009 Irwin and Joan Jacobs Fellowship, Cornell University


Research and Educational Interests

Big Medical Data, Brain Dynamics, Health Informatics, Machine Learning, Data Mining

Background Education

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

Professional Activities

2015 Publicity Chair, IEEE International Conference on Machine Learning and Applications

2014 Guest Editor, Special Issue on Sparsity Techniques in Medical Imaging, Computerized Medical Imaging and Graphics

2014 Co-Chair, International Workshop on Sparsity Techniques in Medical Imaging, in conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Boston, MA

Program Committee / Reviewers, Medical Image Analysis (IF=4.5), IEEE Transactions on Medical Imaging (IF=4.3), ACM Computing Survey (IF=3.7), Neuroradiology (IF=2.4), IEEE Multimedia (IF=1.7), CVPR, ICCV, MICCAI, ICIP, ISBI.

Professional Experience

2014 – Present Assistant Professor, School of Computing and Information Sciences, Florida International University, Miami, FL 33199

Selected Publications

  • Fang, R., Pouyanfar, S., Yang, Y., Chen, S.C. and Iyengar, S.S., 2016. Computational Health Informatics in the Big Data Age: A Survey. ACM Computing Surveys (CSUR), 49(1), p.12.
  • Fang, R., Zhang, S., Chen, T. and Sanelli, P.C., 2015. Robust low-dose CT perfusion deconvolution via tensor total-variation regularization. IEEE Transactions on Medical Imaging, 34(7), pp.1533-1548.
  • Fang, R., Karlsson, K., Chen, T. and Sanelli, P.C., 2014. Improving low-dose blood–brain barrier permeability quantification using sparse high-dose induced prior for Patlak model. Medical image analysis, 18(6), pp.866-880.
  • Fang, R., Chen, T. and Sanelli, P.C., 2013. Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning. Medical image analysis, 17(4), pp.417-428.
  • Fang, R., Tang, K.D., Snavely, N. and Chen, T., 2010, September. Towards computational models of kinship verification. In 2010 IEEE International Conference on Image Processing (pp. 1577-1580). IEEE. (Best Paper Award)