John Zimmerman

About

John Zimmerman is an Associate Professor with a joint appointment between the School of Design and the Human-Computer Interaction Institute at Carnegie Mellon University. He teaches studios and seminars on interaction design as well as a course on mobile service innovation. His research spans four key areas: the value of digital things, service design and social computing, ubiquitous and mobile computing, and research through design. In the area of digital possessions, he investigates why people often view their digital items as less valuable than traditional objects and explores how changes in form and behavior can enhance their perceived value. His recent projects include an interactive bedroom for teens, an alarm clock for children, and a service that sends postcards from the past. In social computing, Zimmerman studies how social technologies can foster citizen engagement in public service design and planning, exemplified by a crowdsourced real-time transit information system that has collected over 150,000 location traces and earned awards from the FCC and the Intelligent Transportation Society of America. His work in ubiquitous and mobile computing examines how smartphones can serve as intelligent platforms supporting new services, such as systems that learn family routines or detect depression through behavioral changes. Additionally, his research through design investigates how design researchers can create new artifacts to explore speculative futures, and he has…

Research topics

• Computer Science
• Cardiology
• Internal medicine
• Medicine
• Computer Security
• Artificial Intelligence
• Psychology
• Public relations
• Internet privacy
• Social psychology

Selected publications

• Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to Design

  2020 · 551 citations

  Senior authorCorresponding

  Artificial Intelligence (AI) plays an increasingly important role in improving HCI and user experience. Yet many challenges persist in designing and innovating valuable human-AI interactions. For example, AI systems can make unpredictable errors, and these errors damage UX and even lead to undesired societal impact. However, HCI routinely grapples with complex technologies and mitigates their unintended consequences. What makes AI different? What makes human-AI interaction appear particularly di…

  DOI

• Social Boundaries for Personal Agents in the Interpersonal Space of the Home

  2020 · 60 citations

  The presence of voice activated personal assistants (VAPAs) in people's homes rises each year [31]. Industry efforts are invested in making interactions with VAPAs more personal by leveraging information from messages and calendars, and by accessing user accounts for 3rd party services. However, the use of personal data becomes more complicated in interpersonal spaces, such as people's homes. Should a shared agent access the information of many users? If it does, how should it navigate issues of…

  DOI

• The Future of HCI-Policy Collaboration

  2024-05-11 · 45 citations

  preprintOpen accessSenior author

  Policies significantly shape computation’s societal impact, a crucial HCI concern. However, challenges persist when HCI professionals attempt to integrate policy into their work or affect policy outcomes. Prior research considered these challenges at the “border” of HCI and policy. This paper asks: What if HCI considers policy integral to its intellectual concerns, placing system-people-policy interaction not at the border but nearer the center of HCI research, practice, and education? What if H…

  PublisherOA PDFDOI

• Sketching AI Concepts with Capabilities and Examples: AI Innovation in the Intensive Care Unit

  2024-05-11 · 28 citations

  preprintOpen accessSenior author

  Advances in artificial intelligence (AI) have enabled unprecedented capabilities, yet innovation teams struggle when envisioning AI concepts. Data science teams think of innovations users do not want, while domain experts think of innovations that cannot be built. A lack of effective ideation seems to be a breakdown point. How might multidisciplinary teams identify buildable and desirable use cases? This paper presents a first hand account of ideating AI concepts to improve critical care medicin…

  PublisherOA PDFDOI

• Predictors of Atrial Fibrillation in Patients With Stroke Attributed to Large- or Small-Vessel Disease

  JAMA Neurology · 2022 · 22 citations

  Importance: The Stroke of Known Cause and Underlying Atrial Fibrillation (STROKE AF) trial found that approximately 1 in 8 patients with recent ischemic stroke attributed to large- or small-vessel disease had poststroke atrial fibrillation (AF) detected by an insertable cardiac monitor (ICM) at 12 months. Identifying predictors of AF could be useful when considering an ICM in routine poststroke clinical care. Objective: To determine the association between commonly assessed risk factors and post…

  DOI

Recent grants

• CHS: Small: Envisioning and Prototyping Artificial Intelligence Product and Service Innovations with User Experience Designers

  NSF · $532k · 2020–2024

• NRI: FND: Human-Robot Collaboration with Distributed and Embodied Intelligence

  NSF · $732k · 2017–2022

Frequent coauthors

• Jodi Forlizzi

  Carnegie Mellon University

  56 shared

• Anthony Tomasic

  Carnegie Mellon University

  34 shared

• Aaron Steinfeld

  Carnegie Mellon University

  33 shared

• Nevenka Dimitrova

  New York Medical College

  18 shared

• Lalitha Agnihotri

  16 shared

• Marion J. Ball

  15 shared

• William Odom

  Simon Fraser University

  13 shared

• Jason Wiese

  University of Utah

  12 shared

Awards & honors

• Awards from the FCC and from the Intelligent Transportation…

Similar researchers at Carnegie Mellon University

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