Bio


James Landay is a Professor of Computer Science and the Anand Rajaraman and Venky Harinarayan Professor in the School of Engineering at Stanford University. He specializes in human-computer interaction. Landay is the co-founder and Co-Director of the Stanford Institute for Human-centered Artificial Intelligence (HAI). Prior to joining Stanford, Landay was a Professor of Information Science at Cornell Tech in New York City for one year and a Professor of Computer Science & Engineering at the University of Washington for 10 years. From 2003-2006, he also served as the Director of Intel Labs Seattle, a leading research lab that explored various aspects of ubiquitous computing. Landay was also the chief scientist and co-founder of NetRaker, which was acquired by KeyNote Systems in 2004. Before that he was an Associate Professor of Computer Science at UC Berkeley. Landay received his BS in EECS from UC Berkeley in 1990, and MS and PhD in Computer Science from Carnegie Mellon University in 1993 and 1996, respectively. His PhD dissertation was the first to demonstrate the use of sketching in user interface design tools. He is a member of the ACM SIGCHI Academy and an ACM Fellow. He is an ACM SIGCHI Lifetime Research Award winner. He served for six years on the NSF CISE Advisory Committee.

Administrative Appointments


  • Co-Director, Stanford Institute for Human-Centered AI (2024 - Present)
  • Vice Director, Stanford Institute for Human-Centered AI (2022 - 2024)
  • Associate Director, Stanford Institute for Human-Centered AI (2018 - 2022)

Honors & Awards


  • Fellow, ACM (2016)
  • SIGCHI Academy Member, ACM SIGCHI (2011)

Boards, Advisory Committees, Professional Organizations


  • CISE Advisory Committee Member, National Science Foundation (2010 - 2016)

Program Affiliations


  • Symbolic Systems Program

Professional Education


  • BS, UC Berkeley, Electrical Engineering & Computer Science (1990)
  • MS, Carnegie Mellon University, Computer Science (1993)
  • PhD, Carnegie Mellon University, Computer Science (1996)

Current Research and Scholarly Interests


Landay's current research interests include Technology to Support Behavior Change (especially for health and sustainability), Demonstrational User Interfaces, Mobile & Ubiquitous Computing, Cross-Cultural Interface Design, Human-Centered AI, and User Interface Design Tools. He has developed tools, techniques, and a top professional book on Web Interface Design.

2024-25 Courses


Stanford Advisees


All Publications


  • Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective. Annual review of public health Hicks, J. L., Boswell, M. A., Althoff, T., Crum, A. J., Ku, J. P., Landay, J. A., Moya, P. M., Murnane, E. L., Snyder, M. P., King, A. C., Delp, S. L. 2022

    Abstract

    Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology-and the unprecedented scope and quantity of data it generates-has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as an exemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors. Expected final online publication date for the Annual Review of Public Health, Volume 44 is April 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

    View details for DOI 10.1146/annurev-publhealth-060220-041643

    View details for PubMedID 36542772

  • Physical workplaces and human well-being: A mixed-methods study to quantify the effects of materials, windows, and representation on biobehavioral outcomes BUILDING AND ENVIRONMENT Douglas, I. P., Murnane, E. L., Bencharit, L., Altaf, B., Costa, J., Yang, J., Ackerson, M., Srivastava, C., Cooper, M., Douglas, K., King, J., Paredes, P. E., Camp, N. P., Mauriello, M., Ardoin, N. M., Markus, H., Landay, J. A., Billington, S. L. 2022; 224
  • Use of Crowdsourced Online Surveys to Study the Impact of Architectural and Design Choices on Wellbeing Frontiers in Sustainable Cities Altaf, B., Bianchi, E., Douglas, I. P., Douglas, K., Byers, B., Paredes, P. E., Ardoin, N. M., Markus, H. R., Murnane, E. L., Bencharit, L. Z., Landay, J. A., Billington, S. L. 2022: 19

    View details for DOI 10.3389/frsc.2022.780376

  • EnglishRot: An Al-Powered Conversational System for Second Language Learning Ruan, S., Jiang, L., Xu, Q., Davis, G. M., Liu, Z., Brunskill, E., Landay, J. A., ASSOC COMP MACHINERY ASSOC COMPUTING MACHINERY. 2021: 434-444
  • Variational Deep Knowledge Tracing for Language Learning Ruan, S., Wei, W., Landay, J., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2021: 323-332
  • Personal identifiability of user tracking data during observation of 360-degree VR video. Scientific reports Miller, M. R., Herrera, F., Jun, H., Landay, J. A., Bailenson, J. N. 2020; 10 (1): 17404

    Abstract

    Virtual reality (VR) is a technology that is gaining traction in the consumer market. With it comes an unprecedented ability to track body motions. These body motions are diagnostic of personal identity, medical conditions, and mental states. Previous work has focused on the identifiability of body motions in idealized situations in which some action is chosen by the study designer. In contrast, our work tests the identifiability of users under typical VR viewing circumstances, with no specially designed identifying task. Out of a pool of 511 participants, the system identifies 95% of users correctly when trained on less than 5min of tracking data per person. We argue these results show nonverbal data should be understood by the public and by researchers as personally identifying data.

    View details for DOI 10.1038/s41598-020-74486-y

    View details for PubMedID 33060713

  • Designing Ambient Narrative-Based Interfaces to Reflect and Motivate Physical Activity. Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference Murnane, E. L., Jiang, X. n., Kong, A. n., Park, M. n., Shi, W. n., Soohoo, C. n., Vink, L. n., Xia, I. n., Xin, Y. n., Yang-Sammataro, J. n., Young, G. n., Zhi, J. n., Moya, P. n., Landay, J. A. 2020; 2020

    Abstract

    Numerous technologies now exist for promoting more active lifestyles. However, while quantitative data representations (e.g., charts, graphs, and statistical reports) typify most health tools, growing evidence suggests such feedback can not only fail to motivate behavior but may also harm self-integrity and fuel negative mindsets about exercise. Our research seeks to devise alternative, more qualitative schemes for encoding personal information. In particular, this paper explores the design of data-driven narratives, given the intuitive and persuasive power of stories. We present WhoIsZuki, a smartphone application that visualizes physical activities and goals as components of a multi-chapter quest, where the main character's progress is tied to the user's. We report on our design process involving online surveys, in-lab studies, and in-the-wild deployments, aimed at refining the interface and the narrative and gaining a deep understanding of people's experiences with this type of feedback. From these insights, we contribute recommendations to guide future development of narrative-based applications for motivating healthy behavior.

    View details for DOI 10.1145/3313831.3376478

    View details for PubMedID 33880463

    View details for PubMedCentralID PMC8055101

  • On-road Guided Slow Breathing Interventions for Car Commuters Balters, S., Landay, J. A., Paredes, P. E., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • BookBuddy: Turning Digital Materials Into Interactive Foreign Language Lessons Through a Voice Chatbot Ruan, S., Willis, A., Xu, Q., Davis, G. M., Jiang, L., Brunskill, E., Landay, J. A., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • Key Phrase Extraction for Generating Educational Question-Answer Pairs Willis, A., Davis, G., Ruan, S., Manoharan, L., Landay, J., Brunskill, E., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019
  • Evaluating Speech-Based Smart Devices Using New Usability Heuristics IEEE PERVASIVE COMPUTING Wei, Z., Landay, J. A. 2018; 17 (2): 84–96
  • FlyMap: Interacting with Maps Projected from a Drone Brock, A. M., Chatain, J., Park, M., Fang, T., Hachet, M., Landay, J. A., Cauchard, J. R., Schmidt, A., Williamson, Elhart, Baldauf, M., Mikusz, M., Sorce, S., Kurdyukova, K., ElAgroudy, P., Gentile ASSOC COMPUTING MACHINERY. 2018
  • Breath Booster! Exploring In-Car, Fast-Paced Breathing Interventions to Enhance Driver Arousal State Balters, S., Murnane, E. L., Landay, J. A., Paredes, P. E., ACM ASSOC COMPUTING MACHINERY. 2018: 128-137
  • Aeroquake: Drone Augmented Dance Kim, H., Landay, J. A., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 691–95
  • Evaluating In-Car Movements in the Design of Mindful Commute Interventions: Exploratory Study. Journal of medical Internet research Paredes, P. E., Hamdan, N. A., Clark, D., Cai, C., Ju, W., Landay, J. A. 2017; 19 (12): e372

    Abstract

    The daily commute could be a right moment to teach drivers to use movement or breath towards improving their mental health. Long commutes, the relevance of transitioning from home to work, and vice versa and the privacy of commuting by car make the commute an ideal scenario and time to perform mindful exercises safely. Whereas driving safety is paramount, mindful exercises might help commuters decrease their daily stress while staying alert. Increasing vehicle automation may present new opportunities but also new challenges.This study aimed to explore the design space for movement-based mindful interventions for commuters. We used qualitative analysis of simulated driving experiences in combination with simple movements to obtain key design insights.We performed a semistructured viability assessment in 2 parts. First, a think-aloud technique was used to obtain information about a driving task. Drivers (N=12) were given simple instructions to complete movements (configural or breath-based) while engaged in either simple (highway) or complex (city) simulated urban driving tasks using autonomous and manual driving modes. Then, we performed a matching exercise where participants could experience vibrotactile patterns from the back of the car seat and map them to the prior movements.We report a summary of individual perceptions concerning different movements and vibrotactile patterns. Beside describing situations within a drive when it may be more likely to perform movement-based interventions, we also describe movements that may interfere with driving and those that may complement it well. Furthermore, we identify movements that could be conducive to a more relaxing commute and describe vibrotactile patterns that could guide such movements and exercises. We discuss implications for design such as the influence of driving modality on the adoption of movement, need for personal customization, the influence that social perception has on participants, and the potential role of prior awareness of mindful techniques in the adoption of new movement-based interventions.This exploratory study provides insights into which types of movements could be better suited to design mindful interventions to reduce stress for commuters, when to encourage such movements, and how best to guide them using noninvasive haptic stimuli embedded in the car seat.

    View details for DOI 10.2196/jmir.6983

    View details for PubMedID 29203458

    View details for PubMedCentralID PMC5735252

  • BrushTouch: Exploring an Alternative Tactile Method for Wearable Haptics Strasnick, E., Cauchard, J. R., Landay, J. A., ACM ASSOC COMPUTING MACHINERY. 2017: 3120–25
  • INQUIRE Tool: Early Insight Discovery for Qualitative Research Paredes, P., Landay, J., Oikonomou, V., Guerrero, R., Yang, T., Karashchuk, P., Jiang, B., Cheshire, C., Canny, J., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2017: 29-32
  • Evaluating In-Car Movements in the Design of Mindful Commute Interventions Journal of Medical Internet Research (JMIR) Paredes, P. E., Hamdan, N. A., Cai, C., Clark, D., Ju, W., Landay, J. 2017: e372

    Abstract

    The daily commute could be a right moment to teach drivers to use movement or breath towards improving their mental health. Long commutes, the relevance of transitioning from home to work, and vice versa and the privacy of commuting by car make the commute an ideal scenario and time to perform mindful exercises safely. Whereas driving safety is paramount, mindful exercises might help commuters decrease their daily stress while staying alert. Increasing vehicle automation may present new opportunities but also new challenges.This study aimed to explore the design space for movement-based mindful interventions for commuters. We used qualitative analysis of simulated driving experiences in combination with simple movements to obtain key design insights.We performed a semistructured viability assessment in 2 parts. First, a think-aloud technique was used to obtain information about a driving task. Drivers (N=12) were given simple instructions to complete movements (configural or breath-based) while engaged in either simple (highway) or complex (city) simulated urban driving tasks using autonomous and manual driving modes. Then, we performed a matching exercise where participants could experience vibrotactile patterns from the back of the car seat and map them to the prior movements.We report a summary of individual perceptions concerning different movements and vibrotactile patterns. Beside describing situations within a drive when it may be more likely to perform movement-based interventions, we also describe movements that may interfere with driving and those that may complement it well. Furthermore, we identify movements that could be conducive to a more relaxing commute and describe vibrotactile patterns that could guide such movements and exercises. We discuss implications for design such as the influence of driving modality on the adoption of movement, need for personal customization, the influence that social perception has on participants, and the potential role of prior awareness of mindful techniques in the adoption of new movement-based interventions.This exploratory study provides insights into which types of movements could be better suited to design mindful interventions to reduce stress for commuters, when to encourage such movements, and how best to guide them using noninvasive haptic stimuli embedded in the car seat.

    View details for DOI 10.2196/jmir.6983

    View details for PubMedCentralID PMC5735252

  • Emotion Encoding in Human-Drone Interaction Cauchard, J. R., Zhai, K. Y., Spadafora, M., Landay, J. A., ACM ASSOC COMPUTING MACHINERY. 2016: 263–70
  • Toolkit Support for Integrating Physical and Digital Interactions HUMAN-COMPUTER INTERACTION Klemmer, S. R., Landay, J. A. 2009; 24 (3): 315-366
  • Integrating physical and digital interactions on walls for fluid design collaboration HUMAN-COMPUTER INTERACTION Klemmer, S. R., Everitt, K. M., Landay, J. A. 2008; 23 (2): 138-213
  • The mobile sensing platform: An embedded activity recognition system IEEE PERVASIVE COMPUTING Choudhury, T., Consolvo, S., Harrison, B., LaMarca, A., LeGrand, L., Rahimi, A., Rea, A., Borriello, G., Hemingway, B., Klasnja, P. P., Koscher, K., Landay, J. A., Lester, J., Wyatt, D., Haehnel, D., Hightower, J. 2008; 7 (2): 32-41
  • Siren: Context-aware computing for firefighting 2nd International Conference on Pervasive Computing Jiang, X. D., Chen, N. Y., Hong, J. I., Wang, K., Takayama, L., Landay, J. A. SPRINGER-VERLAG BERLIN. 2004: 87–105