About

Oded Nov is a Professor at NYU Tandon School of Engineering, serving as the Department Chair of Technology Management and Innovation. His research interests include human-computer interaction, the future of work, human-AI interaction, and digital health. He holds a Ph.D. from Cambridge University, an M.Sc. from the London School of Economics, and a B.A. from Tel Aviv University. Nov has received numerous awards and grants, including the NYU Tandon Excellence in Research Award, the National Science Foundation CAREER Award, and the National Academies Keck Futures Award. His work encompasses a broad range of projects related to digital health, technology-facilitated healthcare, urban science, and socio-technical systems, contributing significantly to understanding and advancing human-centered technology and its applications in health and urban environments.

Research topics

• Computer Science
• Virology
• Political Science
• Economics
• Artificial Intelligence
• Medicine
• Data Mining
• Real-time computing
• Engineering
• Geography
• Epistemology
• Mathematics
• World Wide Web
• Economic growth
• Pathology
• Internal medicine
• Microeconomics

Selected publications

• Studying the Separability of Visual Channel Pairs in Symbol Maps

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  Visualizations often encode multivariate data by mapping attributes to distinct visual channels such as color, size, or shape. The effectiveness of these encodings depends on separability--the extent to which channels can be perceived independently. Yet systematic evidence for separability, especially in map-based contexts, is lacking. We present a crowdsourced experiment that evaluates the separability of four channel pairs--color (ordered) x shape, color (ordered) x size, size x shape, and size x orientation--in the context of bivariate symbol maps. Both accuracy and speed analyses show that color x shape is the most separable and size x orientation the least separable, while size x color and size x shape do not differ. Separability also proved asymmetric--performance depended on which channel encoded the task-relevant variable, with color and shape outperforming size, and square shape especially difficult to discriminate. Our findings advance the empirical understanding of visual separability, with implications for multivariate map design.

  PublisherOA PDFDOI

• Counting the Wait: Effects of Temporal Feedback on Downstream Task Performance and Perceived Wait-Time Experience during System-Imposed Delays

  arXiv (Cornell University) · 2026-02-04

  articleOpen accessSenior author

  System-imposed wait times can significantly disrupt digital workflows, affecting user experience and task performance. Prior HCI research has examined how temporal feedback, such as feedback mode (Elapsed-Time vs. Remaining-Time) shapes wait-time perception. However, few studies have investigated how such feedback influences users' downstream task performance, as well as overall affective and cognitive experience. To study these effects, we conducted an online experiment where 425 participants performing a visual reasoning task experienced a 10-, 30-, or 60-second wait with a Remaining-Time, Elapsed-Time, or No Time Display. Findings show that temporal feedback mode shapes how waiting is perceived: Remaining-Time feedback increased frustration relative to Elapsed-Time feedback, while No Time Display made waits feel longer and heightened ambiguity. Notably, these experiential differences did not translate into differences in post-wait task performance. Integrating psychophysical and cognitive science perspectives, we discuss implications for implementing temporal feedback in latency-prone digital systems.

  PublisherOA PDF

• Autonomy or guidance: What users want from AI versus human advisors

  ACM AI Letters · 2026-05-06

  articleSenior author

  As agentic artificial intelligence (AI) increasingly serves as a source of expert advice, designers face critical moral questions: should AI advisors optimize outcomes, guide users, or present information neutrally? This paper investigates laypeople’s preferences for four advice designs inspired by moral debates about outcomes versus agency: outcome efficacy, outcome expectancy, complete agency, and guided agency. Across two advice scenarios in health and finance, participants consistently rejected designs that restrict user autonomy. Participants favored "complete agency" (information only) when receiving AI advice but "guided agency" (information and recommendation) from human experts, suggesting that people assign different normative roles to AI and human experts. This asymmetry has implications for AI system design, regulation, and ethics. As AI advice becomes more prevalent, respecting users’ autonomy and expectations is crucial not only for usability, but also for ensuring that algorithmic systems remain aligned with public values.

  PublisherDOI

• Investigating Writing Professionals' Relationships with GenAI: How Combined Perceptions of Rivalry and Collaboration Shape Work Practices and Outcomes

  2026-04-13 · 1 citations

  articleOpen access

  This study investigates how professional writers’ complex relationship with GenAI shapes their work practices and outcomes. Through a cross-sectional survey with writing professionals (n=403) in diverse roles, we show that collaboration and rivalry orientation are associated with differences in work practices and outcomes. Rivalry is primarily associated with relational crafting and skill maintenance. Collaboration is primarily associated with task crafting, productivity, and satisfaction, at the cost of long-term skill deterioration. Combination of the orientations (high rivalry and high collaboration) reconciles these differences, while boosting the association with the outcomes. Our findings argue for a balanced approach where high levels of rivalry and collaboration are essential to shape work practices and generate outcomes aimed at the long-term success of the job. We present key design implications on how to increase friction (rivalry) and reduce over-reliance (collaboration) to achieve a more balanced relationship with GenAI.

  PublisherDOI

• The Impact of Response Latency and Task Type on Human-LLM Interaction and Perception

  arXiv (Cornell University) · 2026-02-09

  preprintOpen accessSenior author

  Responsiveness in large language model (LLM) applications is widely assumed to be critical, yet the impact of latency on user behavior and perception of output quality has not been systematically explored. We report a controlled experiment varying time-to-first-token latency (2, 9, 20 seconds) across two taxonomy-driven knowledge task types (Creation and Advice). Log analyses reveal that user interaction behaviors were robust to latency, yet varied by task type: Creation tasks elicited more frequent prompting than Advice tasks. In contrast, participants who experienced 2-second latencies rated the LLM's outputs less thoughtful and useful than those who experienced 9- or 20-second latencies. Participants attributed delays to AI deliberation, though long waits occasionally shifted this interpretation toward frustration or concerns about reliability. Overall, this work demonstrates that latency is not simply a cost to reduce but a tunable design variable with ethical implications. We offer design strategies for enhancing human-LLM interaction.

  PublisherDOI

• Counting the Wait: Effects of Temporal Feedback on Downstream Task Performance and Perceived Wait-Time Experience during System-Imposed Delays

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  System-imposed wait times can significantly disrupt digital workflows, affecting user experience and task performance. Prior HCI research has examined how temporal feedback, such as feedback mode (Elapsed-Time vs. Remaining-Time) shapes wait-time perception. However, few studies have investigated how such feedback influences users’ downstream task performance, as well as overall affective and cognitive experience. To study these effects, we conducted an online experiment where 425 participants performing a visual reasoning task experienced a 10-, 30-, or 60-second wait with a Remaining-Time, Elapsed-Time, or No Time Display. Findings show that temporal feedback mode shapes how waiting is perceived: Remaining-Time feedback increased frustration relative to Elapsed-Time feedback, while No Time Display made waits feel longer and heightened ambiguity. Notably, these experiential differences did not translate into differences in post-wait task performance. Integrating psychophysical and cognitive science perspectives, we discuss implications for implementing temporal feedback in latency-prone digital systems.

  PublisherDOI

• Investigating Writing Professionals' Relationships with Generative AI: How Combined Perceptions of Rivalry and Collaboration Shape Work Practices and Outcomes

  Open MIND · 2026-01-01

  article

  This study investigates how professional writers' complex relationship with GenAI shapes their work practices and outcomes. Through a cross-sectional survey with writing professionals (n=403) in diverse roles, we show that collaboration and rivalry orientation are associated with differences in work practices and outcomes. Rivalry is primarily associated with relational crafting and skill maintenance. Collaboration is primarily associated with task crafting, productivity, and satisfaction, at the cost of long-term skill deterioration. Combination of the orientations (high rivalry and high collaboration) reconciles these differences, while boosting the association with the outcomes. Our findings argue for a balanced approach where high levels of rivalry and collaboration are essential to shape work practices and generate outcomes aimed at the long-term success of the job. We present key design implications on how to increase friction (rivalry) and reduce over-reliance (collaboration) to achieve a more balanced relationship with GenAI.

• Studying the Separability of Visual Channel Pairs in Symbol Maps

  arXiv (Cornell University) · 2026-02-23

  preprintOpen accessSenior author

  Visualizations often encode multivariate data by mapping attributes to distinct visual channels such as color, size, or shape. The effectiveness of these encodings depends on separability--the extent to which channels can be perceived independently. Yet systematic evidence for separability, especially in map-based contexts, is lacking. We present a crowdsourced experiment that evaluates the separability of four channel pairs--color (ordered) x shape, color (ordered) x size, size x shape, and size x orientation--in the context of bivariate symbol maps. Both accuracy and speed analyses show that color x shape is the most separable and size x orientation the least separable, while size x color and size x shape do not differ. Separability also proved asymmetric--performance depended on which channel encoded the task-relevant variable, with color and shape outperforming size, and square shape especially difficult to discriminate. Our findings advance the empirical understanding of visual separability, with implications for multivariate map design.

  PublisherDOI

• The Impact of Response Latency and Task Type on Human-LLM Interaction and Perception

  2026-04-13 · 1 citations

  articleOpen accessSenior author

  Responsiveness in large language model (LLM) applications is widely assumed to be critical, yet the impact of latency on user behavior and perception of output quality has not been systematically explored. We report a controlled experiment varying time-to-first-token latency (2, 9, 20 seconds) across two taxonomy-driven knowledge task types (Creation and Advice). Log analyses reveal that user interaction behaviors were robust to latency, yet varied by task type: Creation tasks elicited more frequent prompting than Advice tasks. In contrast, participants who experienced 2-second latencies rated the LLM’s outputs less thoughtful and useful than those who experienced 9- or 20-second latencies. Participants attributed delays to AI deliberation, though long waits occasionally shifted this interpretation toward frustration or concerns about reliability. Overall, this work demonstrates that latency is not simply a cost to reduce but a tunable design variable with ethical implications. We offer design strategies for enhancing human-LLM interaction.

  PublisherDOI

• The Impact of Response Latency and Task Type on Human-LLM Interaction and Perception

  OSF Preprints (OSF Preprints) · 2026-01-25

  otherOpen accessSenior author
  Publisher

Recent grants

• Learning Data Science Through Civic Engagement With Open Data

  NSF · $300k · 2020–2023

• CHS: Small: Collaborative Research: Human-computer interaction for personal genomics: understanding, informing, and empowering users

  NSF · $171k · 2014–2018

• VOSS: Collaborative Research: Agency, Structure and Organization: Paths to Participation in Large-Scale Socio-Technical Systems

  NSF · $153k · 2014–2018

• CAREER: Individual Attributes and Social Participation: Designing for Citizen Science

  NSF · $532k · 2012–2018

• Citizen Science uncovers Brooklyn Atlantis: An inter-disciplinary exploration of the dynamics of networks of humans and machines in peer production settings

  NSF · $560k · 2011–2016

Frequent coauthors

• Ofer Arazy

  University of Haifa

  63 shared

• Maurizio Porfiri

  51 shared

• David Mann

  NYU Langone Health

  35 shared

• Andrew Caplin

  30 shared

• John Leahy

  29 shared

• Dániel Csaba

  29 shared

• Jeffrey Laut

  New York University

  28 shared

• Nanda Kumar

  27 shared

Labs

• Oded Nov LabPI

  Digital health, Human-computer interaction, Citizen science, HCI and decision-making

Awards & honors

• Marie Curie Fellowship (2004)
• The National Academies Keck Futures Award: Toward an Integra…
• The National Academies Keck Futures Award: Uncovering the DN…
• Google Focused Research Award: Examining the Impact of Socia…
• Financial Industry Regulatory Authority (FINRA) Foundation:…