James Fogarty

About

James Fogarty is a Professor of Computer Science & Engineering at the University of Washington. He is a core member of the DUB Group, a cross-campus initiative that advances research and education in Human-Computer Interaction and Design. His broad research interests encompass Human-Computer Interaction, User Interface Software and Technology, and Ubiquitous Computing. Professor Fogarty focuses on developing, deploying, and evaluating new approaches to personal data and human-AI interaction, with particular applications in health and accessibility. His research is conducted collaboratively with a group of colleagues and current advisees. His work has received direct support from the National Science Foundation, the National Library of Medicine, and the Agency for Healthcare Research and Quality, as well as generous support from industry partners including Adobe, FXPAL, Google, Intel, Microsoft, and Nokia.

Research topics

• Computer Science
• Medicine
• Psychology
• Artificial Intelligence
• Internet privacy
• Developmental psychology
• Psychiatry
• Environmental health
• Data science
• Engineering

Selected publications

• DreamCatcher: Exploring How Parents and School-Age Children can Track and Review Sleep Information Together

  Proceedings of the ACM on Human-Computer Interaction · 2020 · 80 citations

  Parents and their school-age children can impact one another's sleep. Most sleep-tracking tools, however, are designed for adults and make it difficult for parents and children to track together. To examine how to design a family-centered sleep tracking tool, we designed DreamCatcher. DreamCatcher is an in-home, interactive, shared display that aggregates data from wrist-worn sleep sensors and self-reported mood. We deployed DreamCatcher as a probe to examine the design space of tracking sleep a…

  DOI

• An Epidemiology-inspired Large-scale Analysis of Android App Accessibility

  ACM Transactions on Accessible Computing · 2020 · 50 citations

  Accessibility barriers in mobile applications (apps) can make it challenging for people who have impairments or use assistive technology to use those apps. Ross et al.’s epidemiology-inspired framework emphasizes that a wide variety of factors may influence an app's accessibility and presents large-scale analysis as a powerful tool for understanding the prevalence of accessibility barriers (i.e., inaccessibility diseases ). Drawing on this framework, we performed a large-scale analysis of free A…

  DOI

• Parallel Journeys of Patients with Cancer and Depression: Challenges and Opportunities for Technology-Enabled Collaborative Care

  Proceedings of the ACM on Human-Computer Interaction · 2020 · 39 citations

  as a conceptual design framework for characterizing challenges that patients and their care team encounter when cancer and psychosocial care journeys interact. We use the challenges discovered through the lens of this framework to highlight and prioritize technology design opportunities for supporting whole-person care for patients with co-morbid cancer and depression.

  DOI

• MigraineTracker: Examining Patient Experiences with Goal-Directed Self-Tracking for a Chronic Health Condition

  2024-05-11 · 22 citations

  articleOpen accessSenior author

  Self-tracking and personal informatics offer important potential in chronic condition management, but such potential is often undermined by difficulty in aligning self-tracking tools to an individual's goals. Informed by prior proposals of goal-directed tracking, we designed and developed MigraineTracker, a prototype app that emphasizes explicit expression of goals for migraine-related self-tracking. We then examined migraine patient experiences in a deployment study for an average of 12+ months…

  PublisherOA PDFDOI

• A Large-Scale Longitudinal Analysis of Missing Label Accessibility Failures in Android Apps

  CHI Conference on Human Factors in Computing Systems · 2022-04-28 · 22 citations

  articleOpen access

  We present the first large-scale longitudinal analysis of missing label accessibility failures in Android apps. We developed a crawler and collected monthly snapshots of 312 apps over 16 months. We use this unique dataset in empirical examinations of accessibility not possible in prior datasets. Key large-scale findings include missing label failures in 55.6% of unique image-based elements, longitudinal improvement in ImageButton elements but not in more prevalent ImageView elements, that 8.8% o…

  PublisherOA PDFDOI

Recent grants

• CAREER: Pixel-Based Interpretation and Modification of Graphical User Interfaces

  NSF · $589k · 2011–2017

• HCC-Small: Investigating and Supporting the Iterative and Exploratory Process of Applying Statistical Machine Learning

  NSF · $506k · 2008–2012

• Open Tools for Self-Tracking, Self Experimentation, and Patient-Provider Collaboration in Symptom Self-Management and Clinical Care

  NIH · $1.3M · 2018–2024

Frequent coauthors

• Sean A. Munson

  35 shared

• Scott E. Hudson

  Carnegie Mellon University

  18 shared

• Jacob O. Wobbrock

  Defense Information School

  16 shared

• Jessica Schroeder

  University of Washington

  15 shared

• Julie A. Kientz

  University of Washington

  14 shared

• Ravi Karkar

  14 shared

• Daniel A. Epstein

  University of California, Irvine

  13 shared

• Jasmine Zia

  Swedish Medical Center

  12 shared

Labs

• James Fogarty's LabPI

  Developing, deploying, and evaluating new approaches to personal data and human-AI interaction, often with applications in health and accessibility.

Similar researchers at University of Washington

• Dieter Foxh-107
• Ali Farhadih-91
• Jeffrey Heerh-80