Danielle Albers

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Data analysis 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. In this paper,(More)
Visualizations often seek to aid viewers in assessing the big picture in the data, that is, to make judgments about aggregate properties of the data. In this paper, we present an empirical study of a representative aggregate judgment task: finding regions of maximum average in a series. We show how a theory of perceptual averaging suggests a visual design(More)
Many visualization tasks require the viewer to make judgments about aggregate properties of data. Recent work has shown that viewers can perform such tasks effectively, for example to efficiently compare the maximums or means over ranges of data. However, this work also shows that such effectiveness depends on the designs of the displays. In this paper, we(More)
This work describes a first step towards the creation of an engineering model for the perception of color difference as a function of size. Our approach is to non-uniformly rescale CIELAB using data from crowdsourced experiments, such as those run on Amazon Mechanical Turk. In such experiments, the inevitable variations in viewing conditions reflect the(More)
In this paper, we introduce overview visualization tools for large-scale multiple genome alignment data. Genome alignment visualization and, more generally, sequence alignment visualization are an important tool for understanding genomic sequence data. As sequencing techniques improve and more data become available, greater demand is being placed on(More)
Many bioinformatics applications construct classifiers that are validated in experiments that compare their results to known ground truth over a corpus. In this paper, we introduce an approach for exploring the results of such classifier validation experiments, focusing on classifiers for regions of molecular surfaces. We provide a tool that allows for(More)
Many bioinformatics applications utilize machine learning techniques to create models for predicting which parts of proteins will bind to targets. Understanding the results of these protein surface binding classifiers is challenging, as the individual answers are embedded spatially on the surface of the molecules, yet the performance needs to be understood(More)
Sequence alignment visualization is an important tool for understanding genomics data. Current approaches have difficulty scaling to the larger data sets becoming available. In this work, we survey recent results from perceptual science and show how they provide ideas for creating more scalable alignment visualization tools. We identify several principles,(More)