Danielle Albers Szafir

Viewers often use dense series visualizations to help make aggregate judgments about different value ranges in a display, such as identifying weeks or months with above or below average performance. However, as datasets grow, effectively supporting these judgments becomes an increasingly complex task. By evaluating the effectiveness of different glyphs and encodings of dense series data, we can construct visualization guidelines to more effectively promote the extraction of aggregate information from dense series data.

Publications

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Danielle Albers, Michael Correll, and Michael Gleicher. “Task-Driven Evaluation of Aggregation in Time Series Visualization.” CHI ’14: Proceedings of the 2014 Annual Conference on Human Factors in Computing Systems, 2014.

Danielle Albers, Michael Correll, Michael Gleicher, and Steve Franconeri. “Ensemble Processing of Color and Shape: Beyond Mean Judgments.” Journal of Vision, 2014.

Michael Correll, Danielle Albers, Steve Franconeri, and Michael Gleicher. “Comparing Averages in Time Series Data.” CHI ’12: Proceedings of the 2012 Annual Conference on Human Factors in Computing Systems, 2012.

Color encodings provide a powerful and intuitive channel for encoding data. However, effective color encoding relies on viewers' abilities to differentiate between different values of an encoding. In this work, we leverage crowdsourcing to model color difference under the ubiquitous conditions commonly experienced in visualization design in order to understand these abilities in practice.

Publications

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Danielle Albers Szafir, Maureen Stone, and Michael Gleicher. “Adapting Color Difference to Design.” Color and Imaging Conference, November 2014.
[MERL Best Student Paper Award]

Maureen Stone, Danielle Albers Szafir, and Vidya Setlur. “An Engineering Model for Color Discriminability as a Function of Size.” Color and Imaging Conference, November 2014.

Sequence visualization has a number of applications from biology to the humanities. Perceptual science, signal processing theory, and generality provide a framework for the design of sequence visualizations that can scale well beyond current approaches. We developed Sequence Surveyor, a prototype for scalable multiple whole-genome alignment overview visualization, and its companion tool TextDNA, a word frequency analysis tool, on the basis of these concepts. These tools leverage an aggregate colorfield overview approach to sequence visualization, demonstrating how perceptual science can inform the design of visualization techniques that remain visually manageable at scale and how signal processing concepts can inform novel aggregation schemes that highlight global trends, outliers, and overall data distributions as the problem scales. These techniques allow us to visualize alignments with over 100 whole bacterial-sized genomes.

Project Page with downloads and sample data

Publications

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Danielle Albers, Colin Dewey, and Michael Gleicher. “Sequence Surveyor: Leveraging Overview for Scalable Genomic Alignment Visualization.” IEEE Transactions of Visualization and Computer Graphics, 17(5), October 2011. In Proceedings of IEEE Information Visualization.

Danielle Albers, Colin Dewey, and Michael Gleicher. “Sequence Surveyor: Leveraging Overview for Large-Scale Genomic Alignment Visualization.” 2011 VizBi: Visualizing Biological Data Poster Session, March 2011.

Danielle Albers and Michael Gleicher. “Poster: Perceptual Principles for Scalable Sequence Alignment Visualization.” 2010 IEEE Information Visualization Poster Proceedings, October 2010.

Danielle Albers and Michael Gleicher. “Perceptual Principles for Scalable Sequence Alignment Visualization.” Proceedings of the 7th Symposium on Applied Perception in Graphics and Visualization, August 2010.

In collaboration with biochemists, we explore visualization designs that support the analysis of molecular data within the context of protein surface structure. We conduct this design exploration at two levels: overview designs for analyzing large corpra of proteins and interactive and perceptual mechanisms for exploring data in structural contexts. These efforts collectively inform the design of effective multiscale visualizations for exploring machine learning results across large corpra of spatial data.

Project Page with downloads and sample data

Publications

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Danielle Albers, Alper Sarikaya, and Michael Gleicher. “Lightness Constancy in Surface Visualization.” Transactions on Visualization and Computer Graphics, 2014 (revised and resubmitted).

Alper Sarikaya, Danielle Albers, and Michael Gleicher. “Visualizing Validation of Protein Surface Classifiers.” Computer Graphics Forum, 33(3), June 2014. In Proceedings of Eurographics Conference on Visualization (EuroVis).

Danielle Albers, Alper Sarikaya, and Michael Gleicher. “Lightness Constancy in Surface Visualization.” Poster Abstracts of IEEE VIS 2013, October 2013.
[Best Poster Award]

Alper Sarikaya, Danielle Albers, and Michael Gleicher. “Understanding Performance of Protein Structural Classifiers.” Poster Abstracts of IEEE VIS 2013, October 2013.

Visualization often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. Increasingly, information visualization tools support such comparisons explicitly, beyond simply allowing a viewer to examine each object individually. We propose a general taxonomy of visual designs for comparison that groups designs into three basic categories and their combinations. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we find that all designs are assembled from the building blocks of juxtaposition, superposition, and explicit encodings.

Database of Surveyed Examples

Publications

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Michael Gleicher, Danielle Albers, Rick Walker, Ilir Jusufi, Charles Hansen, and Jonathan Roberts. “Visual Comparison for Information Visualization.” Information Visualization, 10(4), October 2011.