Bio

James Landay is a Professor of Computer Science at Stanford University, specializing in human-computer interaction (HCI). Previously, Dr. Landay was a Professor of Information Science at Cornell Tech in New York City and prior to that a Professor of Computer Science & Engineering at the University of Washington. His current research interests include Technology to Support Behavior Change, Demonstrational Interfaces, Mobile & Ubiquitous Computing, and User Interface Design Tools. He is the founder and co-director of the World Lab, a joint research and educational effort with Tsinghua University in Beijing.

Dr. 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 was previously the Laboratory Director of Intel Labs Seattle, a university affiliated research lab that explored the new usage models, applications, and technology for ubiquitous computing. He was also the chief scientist and co-founder of NetRaker, which was acquired by KeyNote Systems in 2004. From 1997 through 2003 he was a professor in EECS at UC Berkeley.

Academic Appointments

Administrative Appointments

  • Associate Director, Stanford Institute for Human-Centered AI (2018 - Present)

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)

Contact

  • Academic
    University - Faculty Department: Computer Science Position: Professor

Additional Info

  • Mail Code: 9035

Current Research and Scholarly Interests

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

Dr. Landay is the founder and co-director of the World Lab, a joint research and educational effort with Tsinghua University in Beijing.

2019-20 Courses

Stanford Advisees

  • Doctoral Dissertation Reader (AC)
    Katie Cheng, Lawrence Kim, Alexa Siu, Octavia Zahrt
  • Postdoctoral Faculty Sponsor
    Jean Marcel dos Reis Costa
  • Doctoral Dissertation Advisor (AC)
    Parastoo Abtahi, Jane E, Sherry Ruan
  • Doctoral Dissertation Co-Advisor (AC)
    Griffin Dietz
  • Master's Program Advisor
    Brynne Hurst, Charlie Jarvis, Pramod Kotipalli, Marisa Kwiatkowski, Zhixiu Liu, Anji Ren, Alwyn Tan
  • Doctoral (Program)
    Jane E, D Metaxa, Sherry Ruan, Jackie Yang

All Publications

  • drone.io: A Gestural and Visual Interface for Human-Drone Interaction Cauchard, J. R., Tamkin, A., Wang, C., Vink, L., Park, M., Fang, T., Landay, J. A., IEEE IEEE. 2019: 153–62

    View details for Web of Science ID 000467295400022

  • InfoLED: Augmenting LED Indicator Lights for Device Positioning and Communication Yang, J., Landay, J. A., ACM ASSOC COMPUTING MACHINERY. 2019: 175–87

    View details for DOI 10.1145/3332165.3347954

    View details for Web of Science ID 000518189200016

  • 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

    View details for DOI 10.1145/3330430.3333643

    View details for Web of Science ID 000507611000030

  • 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

    View details for DOI 10.1145/3330430.3333636

    View details for Web of Science ID 000507611000020

  • QuizBot: A Dialogue-based Adaptive Learning System for Factual Knowledge Ruan, S., Jiang, L., Xu, J., Tham, B., Qiu, Z., Zhu, Y., Murnane, E. L., Brunskill, E., Landay, J. A., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019

    View details for DOI 10.1145/3290605.3300587

    View details for Web of Science ID 000474467904049

  • Poirot: A Web Inspector for Designers Tanner, K., Johnson, N., Landay, J. A., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019

    View details for DOI 10.1145/3290605.3300758

    View details for Web of Science ID 000474467906064

  • Beyond The Force: Using Quadcopters to Appropriate Objects and the Environment for Haptics in Virtual Reality Abtahi, P., Landry, B., Yang, J., Pavone, M., Follmer, S., Landay, J. A., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2019

    View details for DOI 10.1145/3290605.3300589

    View details for Web of Science ID 000474467904051

  • Evaluating Speech-Based Smart Devices Using New Usability Heuristics IEEE PERVASIVE COMPUTING Wei, Z., Landay, J. A. 2018; 17 (2): 84–96

    View details for DOI 10.1109/MPRV.2018.022511249

    View details for Web of Science ID 000435355100012

  • From on Body to Out of Body User Experience Landay, J. A., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 1–2

    View details for DOI 10.1145/3172944.3176183

    View details for Web of Science ID 000458192600001

  • 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

    View details for DOI 10.1145/3205873.3205877

    View details for Web of Science ID 000482943500013

  • Aeroquake: Drone Augmented Dance Kim, H., Landay, J. A., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 691–95

    View details for DOI 10.1145/3196709.3196798

    View details for Web of Science ID 000478673400060

  • 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

  • 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 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

    View details for DOI 10.1145/3025453.3025759

    View details for Web of Science ID 000426970503007

  • ActiVibe: Design and Evaluation of Vibrations for Progress Monitoring Cauchard, J. R., Cheng, J. L., Pietrzak, T., Landay, J. A., ACM ASSOC COMPUTING MACHINERY. 2016: 3261–71

    View details for DOI 10.1145/2858036.2858046

    View details for Web of Science ID 000380532903026

  • Emotion Encoding in Human-Drone Interaction Cauchard, J. R., Zhai, K. Y., Spadafora, M., Landay, J. A., ACM ASSOC COMPUTING MACHINERY. 2016: 263–70

    View details for Web of Science ID 000389809100036

  • Drone & Me: An Exploration Into Natural Human-Drone Interaction Cauchard, J. R., Jane, L. E., Zhai, K. Y., Landay, J. A., ACM ASSOC COMPUTING MACHINERY. 2015: 361–65

    View details for DOI 10.1145/2750858.2805823

    View details for Web of Science ID 000383742200032

  • Toolkit Support for Integrating Physical and Digital Interactions HUMAN-COMPUTER INTERACTION Klemmer, S. R., Landay, J. A. 2009; 24 (3): 315-366

    View details for DOI 10.1080/07370020902990428

    View details for Web of Science ID 000266871300002

  • 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

    View details for DOI 10.1080/07370020802016399

    View details for Web of Science ID 000257541400002

  • 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

    View details for Web of Science ID 000255249500007

  • 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

    View details for Web of Science ID 000189502500006