Empowering People through Data
I believe that improving the public’s trust and understanding of data is a critical societal challenge. My research focuses on how data representation and data tools can improve our engagement with data in the public sphere. I am particularly interested in the ways that poor assumptions about where people live, their access to technology, and their experiences undermine the impact the way we see and use data. My hope is that we can reimagine the processes and mechanisms we use to share data with more diverse communities.
My current work is amplified by wonderful collaborations with Arvind Satyanarayan at MIT’s Visualization Group (where I was a Visiting Scientist during the ‘21/’22 academic year), Lane Harrison at Worcester Polytechnic Institute, and Narges Mahyar at UMass Amherst. At Bucknell, my research prioritizes collaboration with undergraduate students.
Highlighted Activity
conference
Best Paper Award
We analyzed 79 visualization papers that used “novice,” “non-expert,” “laypeople,” or “general public”. We found ambiguity in novice definitions between papers and a misalignment between the broad language describing novices vs. the population representing them in evaluations (young people, students, and US residents)
CHI: ACM Conference on Human Factors in Computing Systems, 2023
workshop / panel / tutorial
I helped co-create Visualization for Social Good - a central venue within the IEEE VIS community for surfacing work that critiques, defines, or explores the impact of data visualization on society. To date, we have run a tutorial (IEEE VIS 2019), a panel (IEEE VIS 2020), and workshops (IEEE VIS 2021 & 2022). Beginning in 2022, I am shifting my role as a primary organizer to a steering committee
IEEE VIS, 2022
conference
Best Paper Award
Many of the guidelines that inform how designers create data visualizations originate in studies that unintentionally exclude different populations. In this paper, we explore which factors may drive attention and trust in rural groups with diverse economic and educational backgrounds. In 42 semistructured interviews in rural Pennsylvania (USA), we find that a complex set of factors inform attitudes and perceptions about data visualization - including educational background, political affiliation, and personal experience
CHI: ACM Conference on Human Factors in Computing Systems, 2019
Activity and Engagement
poster
IEEE VIS Posters, 2024
journal
IEEE TVCG: Transactions on Visualization and Computer Graphics (VIS 2024), 2024
workshop
Co-organizer: This workshop aims to facilitate and deepen critical discussions about how to define, study, and improve visualization literacy
CHI: ACM Conference on Human Factors in Computing Systems, 2024
conference
Best Paper Award
We analyzed 79 visualization papers that used “novice,” “non-expert,” “laypeople,” or “general public”. We found ambiguity in novice definitions between papers and a misalignment between the broad language describing novices vs. the population representing them in evaluations (young people, students, and US residents)
CHI: ACM Conference on Human Factors in Computing Systems, 2023
journal
IEEE TVCG: Transactions on Visualization and Computer Graphics, 2023
workshop / panel / tutorial
I helped co-create Visualization for Social Good - a central venue within the IEEE VIS community for surfacing work that critiques, defines, or explores the impact of data visualization on society. To date, we have run a tutorial (IEEE VIS 2019), a panel (IEEE VIS 2020), and workshops (IEEE VIS 2021 & 2022). Beginning in 2022, I am shifting my role as a primary organizer to a steering committee
IEEE VIS, 2022
contribution
In the creation of this guide (I am not an author!), I was interviewed by Jonathan Schwabish and Alice Feng to hear about how I approach inclusivity in my visualization work. Our 'Data is Personal' project is featured a few times throughout the guide.
conference
Best Paper Award
Many of the guidelines that inform how designers create data visualizations originate in studies that unintentionally exclude different populations. In this paper, we explore which factors may drive attention and trust in rural groups with diverse economic and educational backgrounds. In 42 semistructured interviews in rural Pennsylvania (USA), we find that a complex set of factors inform attitudes and perceptions about data visualization - including educational background, political affiliation, and personal experience
CHI: ACM Conference on Human Factors in Computing Systems, 2019
conference
EuroVis (Short Paper): EG/VGTC Conference on Visualization, 2019
journal
IEEE TVCG: Transactions on Visualization and Computer Graphics (InfoVis 2018), 2019
workshop
Using Participatory Approaches to Uncover Privacy Norms with Marginalized Communities
Neworked Privacy Workshop at ACM CSCW, 2018
workshop
ACM CHI Workshop on Sensemaking in a Senseless World, 2018
conference
CHI: ACM Conference on Human Factors in Computing Systems, 2018
poster
The Impact of Text-Based Search in Interactive Data Visualizations on the Web
IEEE Conference on Information Visualization (InfoVis 2017), 2017
conference
CHI: ACM Conference on Human Factors in Computing Systems, 2017
late-breaking work
CHI Extended Abstracts: ACM Conference on Human Factors in Computing Systems, 2017
journal
IEEE TVCG: Transactions on Visualization and Computer Graphics (InfoVis 2016), 2017
journal
IEEE TVCG: Transactions on Visualization and Computer Graphics (InfoVis 2015), 2016
conference
CHI: ACM Conference on Human Factors in Computing Systems, 2013
workshop
BELIV: Beyond Time and Errors: Novel Evaluation Methods for Visualization, 2012