Danielle Albers Szafir

schoolUniversity of Coloradobar_chart#945 Global Rank

Total Papers

HCI Rank

#945

Research Areas

Years Active

Recent Publications

Latest papers from Danielle Albers Szafir

CHI '26CorrespondingHonorable MentionInteractive Data Visualization +1

Redundant is Not Redundant: Automating Efficient Categorical Palettes Design Unifying Color & Shape Encodings with CatPAW

Colors and shapes are commonly used to encode categories in multi-class scatterplots. Designers often combine the two channels to create redundant encodings, aiming to enhance class distinctions. However, evidence for the effectiveness of redundancy remains conflicted, and guidelines for constructing effective combina…

CHI '25CorrespondingInteractive Data Visualization +1

Uncovering How Scatterplot Features Skew Visual Class Separation

Multi-class scatterplots are essential for visually comparing data, such as examining class distributions in dimensionality reduction and evaluating classification models. Visual class separation (VCS) measures quantify human perception but are largely derived from and evaluated with datasets reflecting limited types…

CHI '24CorrespondingInteractive Data Visualization +1

Do You See What I See? A Qualitative Study Eliciting High-Level Visualization Comprehension

Designers often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to naturally extract complex, contextualized, and interconnected patterns in data. While limited prior work has studied general high-level interpretation, prevailing perceptual studies of…

CHI '23CorrespondingInteractive Data Visualization +2

Measuring Categorical Perception in Color-Coded Scatterplots

Scatterplots commonly use color to encode categorical data. However, as datasets increase in size and complexity, the efficacy of these channels may vary. Designers lack insight into how robust different design choices are to variations in category numbers. This paper presents a crowdsourced experiment measuring how t…

CHI '22CorrespondingData Physicalization

Making Data Tangible: A Cross-disciplinary Design Space for Data Physicalization

Designing a data physicalization requires a myriad of different considerations. Despite the cross-disciplinary nature of these considerations, research currently lacks a synthesis across the different communities data physicalization sits upon, including their approaches, theories, and even terminologies. To bridge th…

CHI '21CorrespondingBest PaperCognitive Impairment & Neurodiversity (Autism, ADHD, Dyslexia) +2

Understanding Data Accessibility for People with Intellectual and Developmental Disabilities

Using visualization requires people to read abstract visual imagery, estimate statistics, and retain information. However, people with Intellectual and Developmental Disabilities (IDDs) often process information differently, which may complicate connecting abstract visual information to real-world quantities. This pop…

CHI '215th+ AuthorHonorable MentionFull-Body Interaction & Embodied Input +1

danceON: Culturally Responsive Creative Computing

Dance provides unique opportunities for embodied interdisciplinary learning experiences that can be personally and culturally relevant. danceON is a system that supports learners to leverage their body movement as they engage in artistic practices across data science, computing, and dance. The technology includes a Do…

CHI '215th+ AuthorImmersion & Presence Research +1

Grand Challenges in Immersive Analytics

Immersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and human-computer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publi…

CHI '19CorrespondingInteractive Data Visualization +1

Measuring the Separability of Shape, Size, and Color in Scatterplots

Scatterplots commonly use multiple visual channels to encode multivariate datasets. Such visualizations often use size, shape, and color as these dimensions are considered separable--dimensions represented by one channel do not significantly interfere with viewers' abilities to perceive data in another. However, recen…

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