About

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 and is the co-founder and Co-Director of the Stanford Institute for Human-centered Artificial Intelligence (HAI). Landay has a distinguished academic background, having earned his BS in Electrical Engineering & Computer Science from UC Berkeley in 1990, and his 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. Prior to his current role at Stanford, he served as a Professor of Information Science at Cornell Tech in New York City for one year, and as a Professor of Computer Science & Engineering at the University of Washington for ten years. He also served as the Director of Intel Labs Seattle from 2003 to 2006, leading research in ubiquitous computing, and was the chief scientist and co-founder of NetRaker, which was acquired by KeyNote Systems in 2004. Landay's research focuses on human-centered artificial intelligence and human-computer interaction, and he has been recognized as a Fellow of the ACM, a member of the ACM SIGCHI Academy, and a recipient of the ACM SIGCHI Lifetime Research Award. He has also served on the NSF CISE Advisory Committee for six years.

Research topics

• Computer Science
• Political Science
• Psychology
• Sociology
• Human–computer interaction
• Social Science
• Data science
• Social psychology
• Machine Learning
• Artificial Intelligence
• Computer Security
• Public relations
• Gender studies
• Operating system
• Management
• World Wide Web
• Mathematics
• Nursing
• Internet privacy
• Medicine
• Multimedia
• Computer vision
• Art

Selected publications

• A framework of digital biomarkers for neurodegenerative diseases

  Nature Reviews Bioengineering · 2026-04-23

  article
  PublisherDOI

• parkersruth/bayesian_pulse_deconvolution: v1.0.0-preprint

  Open MIND · 2026-02-10

  other

  Preprint version release

  DOI

• Comparing Design Metaphors and User-Driven Metaphors for Interaction Design

  arXiv (Cornell University) · 2026-03-29

  preprintOpen access

  Metaphors enable designers to communicate their ideal user experience for platforms. Yet, we often do not know if these design metaphors match users' actual experiences. In this work, we compare design and user metaphors across three different platforms: ChatGPT, Twitter, and YouTube. We build on prior methods to elicit 554 user metaphors, as well as ratings on how well each metaphor describes users' experiences. We then identify 21 design metaphors by analyzing each platform's historical web presence since their launch date. We find that design metaphors often do not match the metaphors that users use to describe their experiences. Even when design and user metaphors do match, the metaphors do not always resonate universally. Through these findings, we highlight how comparing design and user metaphors can help to evaluate and refine metaphors for user experience.

  PublisherDOI

• Vascular waveform analysis using Bayesian pulse deconvolution

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-02-11

  articleOpen access

  Vascular waveforms, which measure bulk flow in blood vessels, are widely used to measure vital signs, diagnose conditions, and predict long-term health outcomes. Analyzing vascular waveforms depends on three fundamentally interdependent tasks: signal filtering, pulse timing detection, and pulse shape extraction. We hypothesized that Bayesian pulse deconvolution can achieve improved performance on all three tasks by solving them jointly. This method uses an analytical, generative model of vascular waveforms with priors informed by physical and biological domain knowledge. In simulations, Bayesian pulse deconvolution achieves better performance on all tasks compared with existing algorithms: 90% reduction of median filtering error, 60% reduction in pulse timing error, and 85% reduction in shape extraction error. The advantages in simulations extend to human recordings of photoplethysmography waveforms. Taking real time-synchronized electrocardiogram R-R intervals as a proxy ground truth, Bayesian pulse deconvolution achieves 40% lower pulse interval estimation error (RMSE =5.1 ms) compared with typical algorithms (RMSE = 8.3 ms, p=1e-10). By extracting more accurate and informative insights from vascular waveforms, Bayesian pulse deconvolution could advance a wide array of health technologies that rely on interpreting signals from blood vessels.

  PublisherDOI

• Making Videos Accessible for Blind and Low Vision Users Using a Multimodal Agent Video Player

  arXiv (Cornell University) · 2026-02-04

  articleOpen access

  Video content remains largely inaccessible to blind and low-vision (BLV) users. To address this, we introduce a prototype that leverages a multimodal agent - powered by a novel conversational architecture using a multimodal large language model (MLLM) - to provide BLV users with an interactive, accessible video experience. This Multimodal Agent Video Player (MAVP) demonstrates that an interactive accessibility mode can be added to a video through multilayered prompt orchestration. We describe a user-centered design process involving 18 sessions with BLV users that showed that BLV users do not just want accessibility features, but desire independence and personal agency over the viewing experience. We conducted a qualitative study with an additional 8 BLV participants; in this, we saw that the MAVP's conversational dialogue offers BLV users a sense of personal agency, fostering collaboration and trust. Even in the case of hallucinations, it is meta-conversational dialogues about AI's limitations that can repair trust.

  PublisherOA PDF

• Multiplicity in Practice: Glitching the Human In/Through Personal Sensing

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  The commitment to multiplicity and pluriversality challenges design to move beyond singular, stable conceptions of the human. Personal sensing systems offer a rich site for examining this challenge because in mediating the human experience, they constitute the human, although typically as a bounded, rational subject. Building on the critical discourse around sensing technologies, we examine what it might mean to make space for multiplicity in sensing. We articulate “purple zone” as an ontologically ambiguous space emerging from crossing boundaries previously naturalized or deemed fixed, and instantiate it through “EDA purple zone,” marking the threshold of in/visibility in Electrodermal Activity sensing. Through a multi-year process, we developed a real-time biofeedback system that surfaces EDA purple zone. Through a two-week study with 24 participants, we examine encounters with purple zone, instances where the relational human emerges through assemblages, and participants’ strategies for navigating such encounters. We conclude by reflecting on the inherent tensions and possibilities for reconstituting the human in/through personal sensing and engaging ontological multiplicity through design.

  PublisherDOI

• Making Videos Accessible for Blind and Low Vision Users Using a Multimodal Agent Video Player

  Open MIND · 2026-02-04

  preprint

  Video content remains largely inaccessible to blind and low-vision (BLV) users. To address this, we introduce a prototype that leverages a multimodal agent - powered by a novel conversational architecture using a multimodal large language model (MLLM) - to provide BLV users with an interactive, accessible video experience. This Multimodal Agent Video Player (MAVP) demonstrates that an interactive accessibility mode can be added to a video through multilayered prompt orchestration. We describe a user-centered design process involving 18 sessions with BLV users that showed that BLV users do not just want accessibility features, but desire independence and personal agency over the viewing experience. We conducted a qualitative study with an additional 8 BLV participants; in this, we saw that the MAVP's conversational dialogue offers BLV users a sense of personal agency, fostering collaboration and trust. Even in the case of hallucinations, it is meta-conversational dialogues about AI's limitations that can repair trust.

  DOI

• Deep Sketch-Based 3D Modeling: A Survey

  arXiv (Cornell University) · 2026-01-22

  articleOpen access

  In the past decade, advances in artificial intelligence have revolutionized sketch-based 3D modeling, leading to a new paradigm known as Deep Sketch-Based 3D Modeling (DS-3DM). DS-3DM offers data-driven methods that address the long-standing challenges of sketch abstraction and ambiguity. DS-3DM keeps humans at the center of the creative process by enhancing the flexibility, usability, faithfulness, and adaptability of sketch-based 3D modeling interfaces. This paper contributes a comprehensive survey of the latest DS-3DM within a novel design space: MORPHEUS. Built upon the Input-Model-Output (IMO) framework, MORPHEUS categorizes Models outputting Options of 3D Representations and Parts, derived from Human inputs (varying in quantity and modality), and Evaluated across diverse User-views and Styles. Throughout MORPHEUS we highlight limitations and identify opportunities for interdisciplinary research in Computer Vision, Computer Graphics, and Human-Computer Interaction, revealing a need for controllability and information-rich outputs. These opportunities align design processes more closely with user' intent, responding to the growing importance of user-centered approaches.

  PublisherOA PDF

• parkersruth/bayesian_pulse_deconvolution: v1.0.0-preprint

  Zenodo (CERN European Organization for Nuclear Research) · 2026-02-10

  otherOpen access

  Preprint version release

  PublisherDOI

• "What Are You Really Trying to Do?": Co-Creating Life Goals from Everyday Computer Use

  arXiv (Cornell University) · 2026-05-01

  preprintOpen accessSenior author

  Recent advances in user modeling make it feasible to conduct open-ended inference over a person's everyday computer use. Despite longstanding visions of systems that deeply understand our actions and the purposes they serve in our lives, existing systems only capture what a person is doing in the moment -- not why they are doing it -- limiting these systems to surface-level support. We introduce striving co-creation, a process for inferring broader life goals from unstructured observations of computer use. Grounded in Activity Theory and Emmons' personal strivings framework, our system progressively constructs a hierarchical representation of a person's activities. Crucially, strivings are difficult to fully resolve from observation alone, as the same action can be driven by many different goals. Our system therefore supports an editing interface that gives people agency over how they are understood by the system, feeding their corrections back into subsequent rounds of striving induction. In a week-long field deployment (N=14), we find that our co-creation process produces strivings that are representative of participants' long-term goals and gives them greater agency than baseline methods.

  PublisherDOI

Recent grants

• Interaction Economics: Instruments that Measure Social-Computational Systems

  NSF · $766k · 2011–2016

• Collaborative Research: Mobilizing Information Technology Systems to Motivate Reduced Energy Consumption and Carbon Dioxide Emissions

  NSF · $161k · 2009–2012

Frequent coauthors

• Jason Hong

  Carnegie Mellon University

  35 shared

• Scott R. Klemmer

  University of California, San Diego

  29 shared

• Sunny Consolvo

  23 shared

• Elizabeth L. Murnane

  Dartmouth College

  20 shared

• Jon E. Froehlich

  University of Washington

  17 shared

• James Lin

  15 shared

• Mark Newman

  University of Michigan–Ann Arbor

  15 shared

• Richard Davis

  15 shared

Labs

• James Landay's LabPI

  Not provided

Education

• Ph.D.

  Stanford University

• M.S.

  University of Washington

• B.S.

  University of Washington

Awards & honors

• Fellow, ACM (2016)
• ACM SIGCHI Academy (2011)
• ACM SIGCHI Lifetime Research Award