
James Landay
Anand Rajaraman and Venky Harinarayan Professor
Computer Science
Web page: http://landay.org
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
-
Professor, Computer Science
-
Associate Director, Institute for Human-Centered Artificial Intelligence (HAI)
-
Member, Wu Tsai Neurosciences Institute
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)
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.
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) -
Independent Studies (13)
- Advanced Reading and Research
CS 499 (Aut, Win, Spr, Sum) - Advanced Reading and Research
CS 499P (Win, Spr) - Curricular Practical Training
CS 390A (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390B (Win, Sum) - Independent Project
CS 399 (Aut, Win, Spr) - Independent Project
CS 399P (Spr) - Independent Work
CS 199 (Aut, Win, Spr, Sum) - Independent Work
CS 199P (Aut, Win, Spr) - Part-time Curricular Practical Training
CS 390D (Aut, Win) - Ph.D. Research Rotation
ME 398 (Spr) - Senior Project
CS 191 (Aut, Win, Spr) - Supervised Undergraduate Research
CS 195 (Aut, Spr) - Writing Intensive Senior Research Project
CS 191W (Aut, Win, Spr)
- Advanced Reading and Research
-
Prior Year Courses
2020-21 Courses
- Cross-Platform Mobile Development
CS 47 (Win) - Designing Solutions to Global Grand Challenges
CS 377E (Aut) - Introduction to Human-Computer Interaction Design
CS 147 (Win)
2019-20 Courses
- Cross-Platform Mobile Development
CS 47 (Aut) - Fair, Accountable, and Transparent (FAT) Deep Learning
CS 335 (Spr) - Introduction to Human-Computer Interaction Design
CS 147 (Aut) - User Interface Design Project
CS 194H (Win)
2018-19 Courses
- AI-Assisted Care
MED 277 (Aut) - Cross-Platform Mobile Development
CS 47 (Aut) - Designing Solutions to Global Grand Challenges
CS 377E (Spr) - Introduction to Human-Computer Interaction Design
CS 147 (Aut) - User Interface Design Project
CS 194H (Win)
- Cross-Platform Mobile Development
Stanford Advisees
-
Doctoral Dissertation Reader (AC)
Basma Altaf, Glenn Davis, Jingyi Li, Sean Liu, Joseph Makokha -
Postdoctoral Faculty Sponsor
Andrea Cuadra -
Doctoral Dissertation Advisor (AC)
Parastoo Abtahi, Griffin Dietz -
Orals Evaluator
Griffin Dietz -
Master's Program Advisor
D M Raisul Ahsan, Ofure Ebhomielen, Charlotte Feng, Katherine Gjertsen, Alejandrina Gonzalez Reyes, Pramod Kotipalli, Xiaohai Liu, Leilenah Mamea, Esmeralda Nava, Alanna Sun, Danielle Tang, Yesenia Ulloa, Jacob Wagner -
Doctoral Dissertation Co-Advisor (AC)
Michelle Lam, Mark Miller, Jacob Ritchie -
Doctoral (Program)
Parastoo Abtahi, Alan Cheng, Griffin Dietz, Nava Haghighi, Matthew Joerke, Jackie Yang
All Publications
-
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
-
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
-
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
-
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
-
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
-
Adaptive Photographic Composition Guidance
ASSOC COMPUTING MACHINERY. 2020
View details for DOI 10.1145/3313831.3376635
View details for Web of Science ID 000696109100104
-
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
-
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
-
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
-
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
-
drone.io: A Gestural and Visual Interface for Human-Drone Interaction
IEEE. 2019: 153–62
View details for Web of Science ID 000467295400022
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
Emotion Encoding in Human-Drone Interaction
ASSOC COMPUTING MACHINERY. 2016: 263–70
View details for Web of Science ID 000389809100036
-
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
-
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
-
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
-
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
-
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