BiographyDanna Gurari is an Assistant Professor in the School of Information at University of Texas at Austin. Her research interests span computer vision, human computation, crowdsourcing, (bio)medical image analysis, and applied machine learning. Dr. Gurari completed her postdoctoral fellowship in the University of Texas at Austin Computer Science department, her PhD in Computer Science at Boston University in 2015, MS in Computer Science at Washington University in St. Louis in 2005, and BS in Biomedical Engineering at Washington University in St. Louis in 2005. She worked in industry for five years, from 2005-2010, at Boulder Imaging and Raytheon. Dr. Gurari received the Researcher Excellence Award from the Boston University computer science department in 2015. She and her collaborators were recognized with the 2017 Honorable Mention Award at CHI, 2014 Best Paper Award for Innovative Idea at MICCAI IMIC, and 2013 Best Paper Award at WACV.
DegreesPh.D., Computer Science 2010 - 2015 Boston University; Advisor: Dr. Margrit Betke
M.S., Computer Science 2004 - 2005 Washington University in St. Louis; Advisor: Dr. William D. Richard
B.S., Biomedical Engineering Major and Philosophy Minor 2000 - 2005 Washington University in St. Louis
Areas Of Specialization
Applied Machine Learning
Biomedical Image Analysis
Medical Image Analysis
Y. Zhao, B. Price, S. Cohen and D. Gurari, "Guided Image Inpainting: Replacing an Image Region by Pulling Content From Another Image," 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) 2019, pp. 1514-1523.
Gurari, D., Zhao, Y., Jain, S.D. et al. Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch. International Journal of Computer Vision (2019) 127: 1198. https://doi.org/10.1007/s11263-019-01172-6
Gurari, D., He, K., Xiong, B. et al. Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s). International Journal of Computer Vision (2018) 126: 714. https://doi.org/10.1007/s11263-018-1065-7
Danna Gurari, Qing Li, Abigale J. Stangl, Anhong Guo, Chi Lin, Kristen Grauman, Jiebo Luo, Jeffrey P. Bigham. VizWiz Grand Challenge: Answering Visual Questions From Blind People. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 3608-3617
Danna Gurari, Suyog Jain, Margrit Betke, Kristen Grauman. Pull the Plug? Predicting If Computers or Humans Should Segment Images. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 382-391View more in Google Scholar
- Best Paper at the IEEE WACV
- Honorable Mention Award at the SIGCHI Conference