Jon E. Froehlich

About

Jon E. Froehlich is the Lab Director of the Makeability Lab at the University of Washington's Computer Science department. He earned his PhD in 2011 from the University of Washington with a dissertation titled 'Sensing and Feedback of Everyday Activities to Promote Environmental Behaviors.' His research focuses on designing, building, and evaluating new interactive tools and techniques to address pressing societal challenges. The Makeability Lab emphasizes both technological innovations that enable new human abilities and an educational mission to help students gain new skills through research, invention, and human-centered design.

Research topics

• Mathematics
• Engineering
• Computer Science
• Mathematics education
• World Wide Web
• Epistemology
• Psychology
• Multimedia
• Human–computer interaction
• Geography

Selected publications

• What Do We Mean by "Accessibility Research"? A Literature Survey of Accessibility Papers in CHI and ASSETS from 1994 to 2019

  2021 · 301 citations

  Accessibility research has grown substantially in the past few decades, yet there has been no literature review of the field. To understand current and historical trends, we created and analyzed a dataset of accessibility papers appearing at CHI and ASSETS since ASSETS' founding in 1994. We qualitatively coded areas of focus and methodological decisions for the past 10 years (2010-2019, N=506 papers), and analyzed paper counts and keywords over the full 26 years (N=836 papers). Our findings high…

  DOI

• ARMath: Augmenting Everyday Life with Math Learning

  2020 · 63 citations

  Senior authorCorresponding

  We introduce ARMath, a mobile Augmented Reality (AR) system that allows ch ildren to discover mathematical concepts in familiar, ord inary objects and engage with math problems in meaningful contexts. Leveraging advanced computer vision, ARMath recognizes everyday objects, visualizes their mathematical attributes, and turns them into tangible or virtual manipulatives. Using the manipulatives, children can solve problems that situate math operations or concepts in specific everyday contexts. Info…

  DOI

• SPECTRA: Personalizable Sound Recognition for Deaf and Hard of Hearing Users through Interactive Machine Learning

  2025-04-25 · 6 citations

  articleOpen access

  Record soundsTrain personalized model Iteratively test Figure 1: Overview of the SPECTRA pipeline.In an interactive machine learning training workfow, users collect audio data samples (left), flter their data into a training dataset (center), and assess their model's performance in a live environment (right).The design includes key elements to support the needs of DHH users during this process, including spectrogram and waveform audio visualizations of audio, data annotating to save useful conte…

  PublisherOA PDFDOI

• ImaginateAR: AI-Assisted In-Situ Authoring in Augmented Reality

  2025-09-27 · 4 citations

  articleOpen access

  While augmented reality (AR) enables new ways to play, tell stories, and explore ideas rooted in the physical world, authoring personalized AR content remains difficult for non-experts, often requiring professional tools and time. Prior systems have explored AI-driven XR design but typically rely on manually defined VR environments and fixed asset libraries, limiting creative flexibility and real-world relevance. We introduce ImaginateAR, the first mobile tool for AI-assisted AR authoring to com…

  PublisherDOI

• Street View for Whom? An Initial Examination of Google Street View's Urban Coverage and Socioeconomic Indicators in the US

  2025-11-03 · 2 citations

  articleOpen accessSenior author

  Street-level imagery is foundational to modern urban informatics research; however, bias from systematic differences in where and when images are captured can obscure important relationships and impact study findings. We examine how Google Street View (GSV) spatio-temporal capture patterns correlate with ACS socioeconomic indicators and also provide a reproducible, open-source data analysis pipeline and dashboard. To demonstrate and evaluate our approach, we study four US cities computing correl…

  PublisherOA PDFDOI

Recent grants

• EXP: BodyVis: Advancing New Science Learning and Inquiry Experiences via Custom Designed Wearable On-Body Sensing and Visualization

  NSF · $550k · 2014–2019

• HCC: Medium: Combining Crowdsourcing and Computer Vision for Street-level Accessibility

  NSF · $1.2M · 2013–2019

• CAREER: A Tangible-Graphical Approach to Engage Young Children in Wearable Design

  NSF · $541k · 2017–2024

Frequent coauthors

• Leah Findlater

  Apple (United States)

  72 shared

• Liang He

  24 shared

• Lee Stearns

  University of Maryland, College Park

  20 shared

• Dhruv Jain

  19 shared

• James A. Landay

  Stanford University

  17 shared

• Michael Saugstad

  University of Washington

  17 shared

• Manaswi Saha

  Accenture (Switzerland)

  16 shared

• Uran Oh

  Ewha Womans University

  14 shared

Labs

• Makeability LabPI

  An advanced research lab in Human-Computer Interaction and AI

Education

• Ph.D., Computer Science

  University of Washington

  2009

• M.S., Computer Science

  University of Washington

  2004

• B.S., Computer Science and Engineering

  University of California, San Diego

  2002

Awards & honors

• 21 Best Paper and Honorable Mention awards
• Sloan Fellowship
• UW Distinguished Dissertation Award
• Google Faculty Research Awards
• UW College of Engineering Outstanding Faculty Award (2021)

Similar researchers at University of Washington

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