James Landay
Denning Co-Director (Acting) of Stanford HAI, Anand Rajaraman and Venky Harinarayan Professor and Senior Fellow at the Stanford Institute for HAI
Computer Science
Web page: http://landay.org
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.
Academic Appointments
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Professor, Computer Science
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Senior Fellow, Institute for Human-Centered Artificial Intelligence (HAI)
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Member, Bio-X
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Member, Wu Tsai Neurosciences Institute
Administrative Appointments
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Co-Director, Stanford Institute for Human-Centered AI (2024 - Present)
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Vice Director, Stanford Institute for Human-Centered AI (2022 - 2024)
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Associate Director, Stanford Institute for Human-Centered AI (2018 - 2022)
Honors & Awards
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Fellow, ACM (2016)
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SIGCHI Academy Member, ACM SIGCHI (2011)
Boards, Advisory Committees, Professional Organizations
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CISE Advisory Committee Member, National Science Foundation (2010 - 2016)
Program Affiliations
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Symbolic Systems Program
Professional Education
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BS, UC Berkeley, Electrical Engineering & Computer Science (1990)
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MS, Carnegie Mellon University, Computer Science (1993)
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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
- Cross-platform Mobile App Development
CS 147L (Aut) - Digital Canvas: An Introduction to UI/UX Design
CS 91SI (Win) - Introduction to Human-Computer Interaction Design
CS 147 (Aut) -
Independent Studies (14)
- Advanced Reading and Research
CS 499 (Aut, Win, Spr, Sum) - Advanced Reading and Research
CS 499P (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390A (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390B (Aut, Win, Spr, Sum) - Independent Project
CS 399 (Aut, Win, Spr, Sum) - Independent Project
CS 399P (Aut, Win, Spr, Sum) - Independent Study
SYMSYS 196 (Aut, Win, Spr, Sum) - Independent Work
CS 199 (Aut, Win, Spr, Sum) - Independent Work
CS 199P (Aut, Win, Spr, Sum) - Master's Degree Project
SYMSYS 290 (Aut, Win, Spr, Sum) - Part-time Curricular Practical Training
CS 390D (Aut, Win, Spr, Sum) - Senior Project
CS 191 (Aut, Win, Spr, Sum) - Supervised Undergraduate Research
CS 195 (Aut, Win, Spr, Sum) - Writing Intensive Senior Research Project
CS 191W (Aut, Win, Spr)
- Advanced Reading and Research
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Prior Year Courses
2023-24 Courses
- Cross-platform Mobile App Development
CS 147L (Aut) - Introduction to Human-Computer Interaction Design
CS 147 (Aut) - User Interface Design Project
CS 194H (Win)
2022-23 Courses
- Cross-Platform Mobile Development
CS 47 (Aut, Win) - Designing Solutions to Global Grand Challenges
CS 377E, DESIGN 297 (Spr) - Introduction to Human-Computer Interaction Design
CS 147 (Aut) - User Interface Design Project
CS 194H (Win)
2021-22 Courses
- Cross-Platform Mobile Development
CS 47 (Win) - Introduction to Human-Computer Interaction Design
CS 147 (Win) - User Interface Design Project
CS 194H (Spr)
- Cross-platform Mobile App Development
Stanford Advisees
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Doctoral Dissertation Reader (AC)
Basma Altaf -
Postdoctoral Faculty Sponsor
Yiwen Dong, Jane E -
Doctoral Dissertation Advisor (AC)
Jackie Yang -
Orals Evaluator
Jackie Yang -
Master's Program Advisor
Maya Avital, Neha Balamurugan, Zachary Chen, Cyan DeVeaux, Blain Engeda, Madison Fan, Sophie Fujiwara, Cristobal Garcia, Defne Genc, Zoe Kaputa, Caitlin Kunchur, Matthew Lee, Isabelle Levent, Cameron Linhares-Huang, Ingrid Nordberg, Abena Ofosu, Lizi Ottens, Drew Silva, Britney Tran, Nicholas Vo, Alissa Vuillier, Eli Waldman, Maria Wang, Andi Xu, Pannisy Zhao -
Doctoral Dissertation Co-Advisor (AC)
Michelle Lam, Yikai Li, Shardul Sapkota, Yujie Tao -
Doctoral (Program)
Beleicia Bullock, Alan Cheng, Elizabeth Childs, Nava Haghighi, Matthew Joerke, Julia Markel, Parker Ruth, Danilo Symonette, Jackie Yang
All Publications
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Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective.
Annual review of public health
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
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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
2022; 224
View details for DOI 10.1016/j.buildenv.2022.109516
View details for Web of Science ID 000862289200005
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Use of Crowdsourced Online Surveys to Study the Impact of Architectural and Design Choices on Wellbeing
Frontiers in Sustainable Cities
2022: 19
View details for DOI 10.3389/frsc.2022.780376
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Variational Deep Knowledge Tracing for Language Learning
ASSOC COMPUTING MACHINERY. 2021: 323-332
View details for DOI 10.1145/3448139.3448170
View details for Web of Science ID 000883342500031
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StoryCoder: Teaching Computational Thinking Concepts Through Storytelling in a Voice-Guided App for Children
ASSOC COMPUTING MACHINERY. 2021
View details for DOI 10.1145/3411764.3445039
View details for Web of Science ID 000758168000002
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EnglishRot: An Al-Powered Conversational System for Second Language Learning
ASSOC COMPUTING MACHINERY. 2021: 434-444
View details for DOI 10.1145/3397481.3450648
View details for Web of Science ID 000747690200052
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Personal identifiability of user tracking data during observation of 360-degree VR video.
Scientific reports
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
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Designing Ambient Narrative-Based Interfaces to Reflect and Motivate Physical Activity.
Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference
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
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Adaptive Photographic Composition Guidance
ASSOC COMPUTING MACHINERY. 2020
View details for DOI 10.1145/3313831.3376635
View details for Web of Science ID 000696109100104
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Supporting Children's Math Learning with Feedback-Augmented Narrative Technology
ASSOC COMPUTING MACHINERY. 2020: 567-580
View details for DOI 10.1145/3392063.3394400
View details for Web of Science ID 000675620600050
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Soundr: Head Position and Orientation Prediction Using a Microphone Array
ASSOC COMPUTING MACHINERY. 2020: 529-537
Abstract
As a lincosamide antibiotic, lincomycin is still important for treating diseases caused by Gram-positive bacteria. Manufacturing of lincomycin needs efforts to, e.g. maximize desirable species and minimizing unwanted fermentation byproducts. Analysis of the lincomycin biosynthetic gene cluster of Streptomyces lincolnensis, lmbB1, was shown to catalyze the conversion of L-dopa but not of L-tyrosine and then further generated the precursor of lincomycin A. Based on the principle of directed breeding, a strain termed as S. lincolnensis 24-2, was obtained in this work. By overexpressing the lmbB1 gene, this strain produces efficacious lincomycin A and suppresses melanin generation, whereas contains unwanted lincomycin B. The good fermentation performance of the mutant-lmbB1 (M-lmbB1) was also confirmed in a 15 L-scale bioreactor, which increased the lincomycin A production by 37.6% compared with control of 6435 u/mL and reduced the accumulation of melanin by 29.9% and lincomycin B by 73.4%. This work demonstrated that the amplification of lmbB1 gene mutation and metabolic engineering could promote lincomycin biosynthesis and might be helpful for reducing the production of other industrially unnecessary byproduct.
View details for DOI 10.1145/3313831.3376427
View details for Web of Science ID 000695438100099
View details for PubMedID 31916478
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Beyond The Force: Using Quadcopters to Appropriate Objects and the Environment for Haptics in Virtual Reality
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3290605.3300589
View details for Web of Science ID 000474467904051
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On-road Guided Slow Breathing Interventions for Car Commuters
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3290607.3312785
View details for Web of Science ID 000482042102073
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InfoLED: Augmenting LED Indicator Lights for Device Positioning and Communication
ASSOC COMPUTING MACHINERY. 2019: 175–87
View details for DOI 10.1145/3332165.3347954
View details for Web of Science ID 000518189200016
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drone.io: A Gestural and Visual Interface for Human-Drone Interaction
IEEE. 2019: 153–62
View details for Web of Science ID 000467295400022
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BookBuddy: Turning Digital Materials Into Interactive Foreign Language Lessons Through a Voice Chatbot
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3330430.3333643
View details for Web of Science ID 000507611000030
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QuizBot: A Dialogue-based Adaptive Learning System for Factual Knowledge
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3290605.3300587
View details for Web of Science ID 000474467904049
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Poirot: A Web Inspector for Designers
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3290605.3300758
View details for Web of Science ID 000474467906064
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Key Phrase Extraction for Generating Educational Question-Answer Pairs
ASSOC COMPUTING MACHINERY. 2019
View details for DOI 10.1145/3330430.3333636
View details for Web of Science ID 000507611000020
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Evaluating Speech-Based Smart Devices Using New Usability Heuristics
IEEE PERVASIVE COMPUTING
2018; 17 (2): 84–96
View details for DOI 10.1109/MPRV.2018.022511249
View details for Web of Science ID 000435355100012
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From on Body to Out of Body User Experience
ASSOC COMPUTING MACHINERY. 2018: 1–2
View details for DOI 10.1145/3172944.3176183
View details for Web of Science ID 000458192600001
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Breath Booster! Exploring In-Car, Fast-Paced Breathing Interventions to Enhance Driver Arousal State
ASSOC COMPUTING MACHINERY. 2018: 128-137
View details for DOI 10.1145/3240925.3240939
View details for Web of Science ID 000614057600015
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Fast & Furious: Detecting Stress with a Car Steering Wheel
ASSOC COMPUTING MACHINERY. 2018
View details for DOI 10.1145/3173574.3174239
View details for Web of Science ID 000509673108018
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Gender-Inclusive Design: Sense of Belonging and Bias in Web Interfaces
ASSOC COMPUTING MACHINERY. 2018
View details for DOI 10.1145/3173574.3174188
View details for Web of Science ID 000509673107049
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FlyMap: Interacting with Maps Projected from a Drone
ASSOC COMPUTING MACHINERY. 2018
View details for DOI 10.1145/3205873.3205877
View details for Web of Science ID 000482943500013
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Aeroquake: Drone Augmented Dance
ASSOC COMPUTING MACHINERY. 2018: 691–95
View details for DOI 10.1145/3196709.3196798
View details for Web of Science ID 000478673400060
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Evaluating In-Car Movements in the Design of Mindful Commute Interventions: Exploratory Study.
Journal of medical Internet research
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
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BrushTouch: Exploring an Alternative Tactile Method for Wearable Haptics
ASSOC COMPUTING MACHINERY. 2017: 3120–25
View details for DOI 10.1145/3025453.3025759
View details for Web of Science ID 000426970503007
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INQUIRE Tool: Early Insight Discovery for Qualitative Research
ASSOC COMPUTING MACHINERY. 2017: 29-32
View details for DOI 10.1145/3022198.3023272
View details for Web of Science ID 000455085000008
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Evaluating In-Car Movements in the Design of Mindful Commute Interventions
Journal of Medical Internet Research (JMIR)
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
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ActiVibe: Design and Evaluation of Vibrations for Progress Monitoring
ASSOC COMPUTING MACHINERY. 2016: 3261–71
View details for DOI 10.1145/2858036.2858046
View details for Web of Science ID 000380532903026
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Emotion Encoding in Human-Drone Interaction
ASSOC COMPUTING MACHINERY. 2016: 263–70
View details for Web of Science ID 000389809100036
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Drone & Me: An Exploration Into Natural Human-Drone Interaction
ASSOC COMPUTING MACHINERY. 2015: 361–65
View details for DOI 10.1145/2750858.2805823
View details for Web of Science ID 000383742200032
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Toolkit Support for Integrating Physical and Digital Interactions
HUMAN-COMPUTER INTERACTION
2009; 24 (3): 315-366
View details for DOI 10.1080/07370020902990428
View details for Web of Science ID 000266871300002
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Integrating physical and digital interactions on walls for fluid design collaboration
HUMAN-COMPUTER INTERACTION
2008; 23 (2): 138-213
View details for DOI 10.1080/07370020802016399
View details for Web of Science ID 000257541400002
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The mobile sensing platform: An embedded activity recognition system
IEEE PERVASIVE COMPUTING
2008; 7 (2): 32-41
View details for Web of Science ID 000255249500007
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Siren: Context-aware computing for firefighting
2nd International Conference on Pervasive Computing
SPRINGER-VERLAG BERLIN. 2004: 87–105
View details for Web of Science ID 000189502500006