HAX Lab @ URI
HAX Lab Univ. of Rhode Island

The Human-Centered Experiential Technologies (HAX) lab follows a user-centered design process to create and maintain public research systems to augment human information interactions. We are human-computer interaction system researchers applying methods from social computing and human-centered AI to create high-quality data and personalized solutions. We continuously improve our real-world systems to attract, motivate, and support everyday users in the wild.

We are currently building new systems and growing the lab! We are open to to collaborations with industry, non-profits, and academia. Please email Shaun if you are a student at URI interested in joining. :)

We are part of the Department of Computer Science and Statistics at the University of Rhode Island (URI). Our research has been supported by and collaborates with The Readability Consortium, Adobe, Google, NASA, and the United States Air Force.

Are you a University of Rhode Island student interested in joining the lab and building systems and conducting research? Awesome! Please fill out this form and Shaun will be in touch. :)

Projects

Public Systems to Enhance Readability for All

Our readability tests and diagnostic tools share a common database structure and libraries to ensure we can conduct future meta-analyses. This lets us combine our in-lab and remote readability work with daily reading behaviors tracked by our Chrome Extensions for readability.

Studying Readability across Cultures (includes ESL Readers)

We are conducting in-lab and remote studies with eye tracking and pupillometry to understand how readers from different cultures and languages read English text. We have developed and run several studies exploring AI for summarizing and preserving the explainability of information using visual summaries, textual summaries, and mind maps. Flexibility AI: with the RTC helped design and engineer the interactive reading passage simulator that preserves keywords and phrases across grade levels 4--12.

Neurodiverse Readers (Adults and Kids)

We have multiple studies underway conducting remote and in-lab readability studies with adults and kids with at least one neurodiverse condition: ADHD, Autism, or Dyslexia. This is a highly understudied area, but it can help us design new technologies to understand the barriers to reading and how to create new technolohies to overcome them.

Readability in Complex Visual Environments for Readers with Vision Difficulties

Many readers with and without accessibility need to read and engage with the text in a complex environment. We are developing Chrome Extensions to improve readability for color-blind readers, encourage lowering reader's digital carbon footprint, readability in AR/VR environements, and extracting text from YouTube coding tutorial videos for students learning to code.

Public Systems to Maintain and Analyze Tabular Data

Drafty: A Smarter Wiki For Data

We develop public data systems to keep data up-to-date by recruiting people to review it from the crowd of people already using it. This continual review allows the maintenance of data to be self-sustaining over time.

computer science open rankings

Rankings are an ideology. Each is biased in its own way. So choose and combine existing rankings to generate your preferred meta ranking for computer science programs in the United States and Canada.

Papers

Towards Fair and Equitable Incentives to Motivate Paid and Unpaid Crowd Contributions
Shaun Wallace, Talie Massachi, Jiaqi Su, Dave B Miller, Jeff Huang
CHI 2025 [to appear]
Chirp: The Impact of Private Online Self-Disclosure on Perceived Social Support
Talie Massachi, John Roy, Lauren Choi, Gabriela Hoefer, Shaun Wallace, Jeff Huang
CSCW 2024
Digital Reading Rulers: Evaluating Inclusively Designed Rulers for Readers With and Without Dyslexia
Aleena Nickalus, Tianyuan Cai, Zoya Bylinskii, Shaun Wallace
CHI 2023
Web Table Formatting Affects Readability on Mobile Devices
Christopher Tensmeyer, Zoya Bylinski, Tianyuan Cai, Dave B Miller, Ani Nenkova, Aleena Niklaus, Shaun Wallace
WWW 2023
Personalized Font Recommendations: Combining ML and Typographic Guidelines to Optimize Readability
Tianyuan Cai, Shaun Wallace, Tina Rezvanian, Jonathan Dobres, Bernard Kerr, Samuel Berlow, Jeff Huang, Ben D Sawyer, Zoya Bylinskii
DIS 2022
Towards Individuated Reading Experiences: Different Fonts Increase Reading Speed for Different Individuals
Shaun Wallace, Zoya Bylinskii, Jon Dobres, Bernard Kerr, Sam Berlow, Rick Treitman, Kathleen Arpin, Dave B Miller, Jeff Huang, Ben Sawyer
TOCHI 2022
Readability Research: An Interdisciplinary Approach
* 27 co-authors who contributed equally
FnT HCI 2022
The TESS Triple-9 Catalog: 999 uniformly vetted candidate exoplanets
Luca Cacciapuoti, Veselin B. Kostov, Marc Kuchner, Elisa V. Quintana, Knicole D. Colón, Jonathan Brande, Susan E. Mullally, Quadry Chance, Jessie L. Christiansen, John P. Ahlers, Marco Z. Di Fraia, Hugo A. Durantini Luca, Riccardo M. Ienco, Francesco Gallo, Lucas T. de Lima, Michiharu Hyogo, Marc Andrés-Carcasona, Aline U. Fornear, Julien S. de Lambilly, Ryan Salik, John M. Yablonsky, Shaun Wallace, Sovan Acharya
MNRAS 2022
Case Studies on the Motivation and Performance of Contributors Who Verify and Maintain In-Flux Tabular Datasets
Shaun Wallace, Alexandra Papoutsaki, Neilly H. Tan, Hua Guo, Jeff Huang
CSCW 2021
Optimizing Electronic Health Records Through Readability
Rachel V Ball, Dave B Miller, Shaun Wallace, Kathlyn Camargo Macias, Mahmoud Ibrahim, Ernesto Robalino Gonzaga, Olga Karasik, Dekai R Rohlsen-Neal, Sarah Barrientos, Edward A Ross, Abdo Asmar, Ashley M Hughes, Peter A Hancock, Ben D Sawyer
HFES 2021
Sketchy: Drawing Inspiration from the Crowd
Shaun Wallace, Brendan Le, Luis Leiva, Aman Haq, Ari Kintisch, Gabrielle Bufrem, Linda Chang, Jeff Huang
CSCW 2020
Drafty: Enlisting Users to be Editors who Maintain Structured Data
Shaun Wallace, Lucy van Kleunen, Marianne Aubin-Le Quere, Abraham Peterkin, Yirui Huang, Jeff Huang
HCOMP 2017