Danna Gurari

Biography

Danna 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.

Degrees

PhD from Boston University computer science department



MS from Washington University in St. Louis computer science department



BS from Washington University in St. Louis biomedical engineering department



Areas Of Specialization

Computer Vision
Crowdsourcing
Applied Machine Learning
Biomedical Image Analysis
Medical Image Analysis

Recent Publications

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. 382-391

View more in Google Scholar

Recent Awards

  • Best Paper at the IEEE WACV
  • Honorable Mention Award at the SIGCHI Conference

Courses

YearSemesterCourse NumberCourseSyllabus
2021SpringINF 385TSpecial Topics in Information Science Introduction to Machine Learning
2020FallINF 385TSpecial Topics in Information Science Crowdsourcing For Computer Vision
2020SpringINF 385TSpecial Topics in Information Science Introduction to Machine Learning
2019FallINF 385TSpecial Topics in Information Science Crowdsourcing For Computer Vision
2019SpringINF 385TSpecial Topics in Information Science Introduction to Machine Learning
2018FallINF 385TSpecial Topics in Information Science Introduction to Machine Learning
2018SpringINF 385TSpecial Topics in Information Science Introduction to Machine Learning
2017FallINF 385TSpecial Topics in Information Science Crowdsourcing for Computer Vision
2017SpringINF 385TSpecial Topics in Information Science Crowdsourcing for Computer Vision