Photo of Danielle Szafir
Assistant Professor
Computer Science

Assistant Professor Danielle Albers Szafir is an information visualization scientist focusing on understanding visual cognition for information visualization and computer graphics. She received a BS from the University of Washington as a NASA Space Grant Scholar and a PhD in Computer Sciences from the University of Wisconsin Madison. Szafir is a founding faculty member in the Department of Information Science at CU. 

Szafir develops interactive visualization systems and techniques for exploring large and complex data in domains ranging from biology to the humanities. Her work focuses on increasing the scalability and comprehensibility of information visualization by quantifying perception and cognition for design. She also explores how vision science might inform more effective visual interfaces for graphical technologies, including mobile devices and augmented reality. She was a Department of Energy BACTER Fellow, received an Honorable Mention for her dissertation from the IEEE Visualization and Graphics Pioneers, has been awarded Best Paper Awards at IEEE VIS and CIC and has worked with Google and Tableau Research. She is an affiliate in the Computer Science Department and the Center for Research Data and Digital Scholarship and a fellow in the Institute of Cognitive Science. 



  • M. Elliott, C. Xiong, C. Nothelfer, & D. Albers Szafir. 2021. A Design Space of Vision Science Methods for Visualization Research. (to appear) IEEE Transactions on Visualization, 2021.
  • K. Reda & D. Albers Szafir.  2021. Rainbows Revisited: Modeling Effective Colormap Design for Graphical Inference. (to appear) In IEEE Transactions on Visualization.
  • M. Whitlock, D. Albers Szafir, & Kenny Gruchalla. 2020. HydrogenAR: Interactive Data-Driven Storytelling for Dispenser Reliability. (to appear) In Proceedings of the International Symposium on Mixed and Augmented Reality (ISMAR), (Virtual–Nov. 9-13, 2020).
  • M. Shi, D. Albers Szafir, & E. Alexander. 2020. A Survey of Data and Encodings in Word Clouds. Presented at Digital Humanities (Ottawa, Canada (virtual)– July 22-24, 2020).
  • Whitlock M., Mitchell J., Pfeufer N., Arnot B., Craig R., Wilson B., Chung B., & Danielle Albers Szafir.  2020. MRCAT: In Situ Prototyping of Interactive AR Environments. International Conference on Virtual and Mixed Reality (VAMR 2020). (Copenhagen, Denmark, July 22-24, 2020).
  • David BurlinsonDanielle Albers Szafir. 2020. Shape size judgments are influenced by fill and contour closure. In Proceedings of the Annual Meeting of the Vision Sciences Society, (St. Pete Beach, Florida (virtual)–June 19-24, 2020).
  • M. Whitlock, D. Leithinger, D. Szafir, & D. Albers Szafir. 2020. Toward Effective Multimodal Interaction in Augmented Reality. 4th Workshop on Immersive Analytics: Envisioning Future Productivity for Immersive Analytics at ACM CHI. Honolulu, Hawaii–April 25-30, 2020).
  • M. WhitlockS. Smart and D. A. Szafir. 2020. Graphical Perception for Immersive Analytics, 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), Atlanta, GA, USA, 2020, pp. 616-625, doi: 10.1109/VR46266.2020.00084. (Atlanta, Georgia (virtual)–March 22-26, 2020). [Best Paper Nominee]
  • Muyang Shi, Danielle Albers Szafir & Eric Alexander. "A Survey of Data and Encodings in Word Clouds." Digital Humanities, 2020.
  • H. Muthukrishnan & D. Albers Szafir. 2019. Using Machine Learning and Visualization for Qualitative Inductive Analyses of Big Data. Machine Learning from User Interaction (MLUI) at IEEE VIS 2019. (Vancouver, Canada–Oct. 20-25, 2019).
  • Matt WhitlockKeke Wu, & Danielle Albers Szafir. 2019. Designing for Mobile and Immersive Visual Analytics in the Field. Transactions on Visualization & Computer Graphics, 2020. In Proceedings of IEEE VIS 2019. (Vancouver, Canada–October 20-25, 2019).
  • Stephen SmartKeke Wu & Danielle Albers Szafir. 2019. Color Crafting: Automating the Construction of Designer Quality Color Ramps. Transactions on Visualization & Computer Graphics. In Proceedings of IEEE VIS 2019. (Vancouver, Canada–October 20-25, 2019).
  • Harshini Muthukrishnan & Danielle Albers Szafir. 2019. Using Machine Learning and Visualization for Qualitative Inductive Analyses of Big Data. Machine Learning from User Interactions Workshop at IEEE VIS 2019. (Vancouver, Canada–October 20-25, 2019). 
  • Keke Wu, Shea Tanis, and Danielle Szafir. 2019. Designing Communicative Visualization for People with Intellectual Developmental Disabilities. Visualization for Communication (VisComm) at IEEE VIS 2019. DOI: (Vancouver, Canada–Oct. 20-25 2019).
  • E. S. Tanis, D. Albers Szafir, & K. Wu.“Accessible Data: Understanding Visualization Literacy and Graphical Perceptions of People with Intellectual and Developmental Disabilities.” Presented at AAIDD, 2019. (Twin Cities, Minnesota–June 24-27, 2019).
  • Stephen Smart and Danielle Albers Szafir. 2019. Measuring the Separability of Shape, Size, and Color in Scatterplots. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19). Association for Computing Machinery, New York, NY, USA, Paper 669, 1–14. DOI: (Glasgow, UK – May 4-9, 2019).
  • M. Whitlock & D. Albers Szafir. 2019. Situated Prototyping of Data-Driven Applications in Augmented Reality. In Interaction Design and Prototyping for Immersive Analytics (CHI '19). (Glasgow, UK–May 4-9, 2019).
  • Pruss, D., Fujinuma, Y., Daughton, A.R., Paul, M.J., Arnot, B., Szafir, D.A., & Boyd-Graber, J. (2019). Zika discourse in the Americas: A multilingual topic analysis of Twitter. PLoS ONE, 14.
  • J. Muesing, L. Burks, M. Iuzzolino, J. Hatlelid, D. Albers Szafir, & N. Ahmed. 2019. Fully Bayesian Human-Machine Data Fusion for Robust Dynamic Target Surveillance and Characterization. AIAA SciTech Forum, 2019. (San Diego, California–Jan. 7-11, 2019).
  • H. Song and D. A. Szafir, "Where's My Data? Evaluating Visualizations with Missing Data," in IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, pp. 914-924, Jan. 2019, doi: 10.1109/TVCG.2018.2864914.
  • Szafir DA. Modeling Color Difference for Visualization Design. IEEE Trans Vis Comput Graph. 2018 Jan;24(1):392-401. doi: 10.1109/TVCG.2017.2744359. Epub 2017 Aug 29. PMID: 28866544.
  • Sarikaya, A., Gleicher, M., & Szafir, D. A. 2018. Design Factors for Summary Visualization in Visual Analytics. Computer Graphics Forum, 37(3), 145–156.

Additional publications