Projects

This study builds on past research bridging spatial visualization, psychology, and information visualization to holistically inform visualization design. We investigate the effects of chosen disciplines in psychology and math & computer science, combined with cognitive abilities and demographic differences, on visual tasks by measuring estimation of Pearson's correlation coefficient in scatterplots. Results reveal mathematicians demonstrate greater accuracy, benefiting from domain expertise. However, psychologists with high spatial skills outperform some mathematicians with lower spatial skills. Spatial visualization, level of education, and age (inversely) correlated with quicker and more accurate responses. Findings prove that domain expertise and spatial cognition affect correlation judgments in scatterplots, supporting that individual differences should inform visualization design. This work introduces psychologists as a new target domain for visualization research and reveals the impact of combined effects of cognitive abilities and domain on the estimation and manipulation of Pearson's correlation coefficient.

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Supplementary Material for full publication to come by the 28th International Conference on Information Visualisation (IV) IEEE

User telemetry data offers valuable insights into user behavior and goals when interacting with digital products and services. However, analyzing vast amounts of event sequence data can be challenging. In this case study, we apply transformer neural networks to telemetry event sequence data, yielding the UserJourney2Vector model, which treats event sequences similarly to natural language to distill complex user journeys into latent vector representations. The model enables understanding of typical and anomalous user paths, prediction of next actions, and user segmentation based on behavior. We used the model to obtain user clusters, predictive journeys, and cluster statistics, then conducted interviews with digital product experts to assess potential applications and impact of the model output. Experts responded positively to the model’s ability to bolster user personas with data-driven insights and noted integrating model outputs with current practices could augment product design and data interpretation.

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Full Article Published by Association for Computing Machinery

This study builds on past research to present a domain-specific empirical investigation of artists and math & computer scientists on their respective relationships to, perceptions of, and interactions with data visualization. We conducted a three-phase study utilizing mixed-methods to investigate performance on visual and text representations of data between domains. Our findings evidenced how math & computer scientists are proficient utilizing text representations of data while artists benefit more from visual chart representations. Finally, we present perspectives from artists to gain an understanding of their approach to visual and mathematical tasks. Our findings indicate that artists are especially adept at statistical visual tasks and that development of cognitive skills could be fostered by individuals to potentially benefit visualization task performance.

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Full Article Published by Association for Computing Machinery

This case study presents an investigation on explainable artificial intelligence (AI) visualization in business applications. Design guidelines for human-AI interaction are broad and touch on a range of user experiences with AI. Oftentimes, guidelines are not specific to enterprise scenarios with late-stage end users with limited AI knowledge and experience. We present a three-phase study on a visual design of a machine learning (ML) algorithm output. We conducted a user study on an existing design with limited visual AI explanation cues, ran a redesign workshop with various design and data experts, and conducted a reassessment with systematically applied AI explanation guidelines in place. We surface how users with various tech proficiency and AI/ML backgrounds interact with designs and how visual explanation cues increase understanding and effective decision making of users with low AI/ML familiarity. This design process corroborated the application and impact of existing guidelines and surfaced specific design implications for AI explainability within enterprise design.

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Full Article Published by Association for Computing Machinery

Understanding one's audience is foundational to creating high impact visualization designs. However, individual differences and cognitive abilities influence interactions with information visualization. Differing user needs and abilities suggest that an individual’s background could influence cognitive performance and interactions with visuals in a systematic way. This study builds on current research in domain-specific visualization and cognition to address if domain and spatial visualization ability combine to affect performance on information visualization tasks. We measure spatial visualization and visual task performance between those with tertiary education and professional profile in business, law & political science, and math & computer science. We conducted an online study with 90 participants using an established psychometric test to assess spatial visualization ability, and bar chart layouts rotated along Cartesian and polar coordinates to assess performance on spatially rotated data. Accuracy and response times varied with domain across chart types and task difficulty. We found that accuracy and time correlate with spatial visualization level, and education in math & computer science can indicate higher spatial visualization. Additionally, we found that higher personal motivations could contribute to increased levels of accuracy. Our findings indicate discipline not only affects user needs and interactions with data visualization, but also cognitive traits. Our results can advance inclusive practices in visualization design and add to knowledge in domain-specific visual research that can empower designers across disciplines to create effective visualizations.

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GitHub Repo for Open Source Flask Web App
Full Article Published by IEEE Transactions on Visualization and Computer Graphics

There is evidence that individual differences and cognitive abilities impact the effectiveness of information visualization. In this study, we test whether spatial visualization ability impacts recall of bar charts. We propose and test a methodology using a crowd-sourced online study to assess spatial visualization (with an established spatial ability assessment) and understand differences in error and response time across low and high spatial individuals. We found that once a task is understood, there is no difference in response time, however, error is affected by complexity and task difficulty.

"What's the Trend? Time Series Visualizations of Market Research"  |  Master's Thesis

Sept 2018     London, UK

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"The Multicultural World of Millennials" Barna Trends 2018  |  Author

Dec 2017     Atlanta, GA, USA

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National Science Foundation and Fulbright Research Grant  |  Team Member

2015-2016     Okahandja, Namibia

Assessed practicability and impact of introducing media solutions in Namibian high school STEM classrooms through interviews with senior education administrators

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Ordog-Tandon Theorem  |  Researcher under Dr. Kendra Killpatrick

May-Aug 2014     Malibu, CA, USA

Developed the Ordog-Tandon Combinatorial Theorem on Tribonacci tableaux and permutations with research partner. Presented research at national Mathematical Association of America 2015 conference.

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Keck Grant Awardee  |  Independent Researcher

Feb 2012     Malibu, CA, USA

Explored a mathematical model for delivering humanitarian aid to developing countries. Conducted field research in Washington D.C. and Seattle, WA interviewing leaders of international nongovernmental organizations, including World Bank, to evaluate availability of data and decision metrics.