About

The Tague Team Lab is an Ecohydrology Research Lab based at the University of California Santa Barbara's Bren School of Environmental Science and Management. The lab uses advanced data science techniques to understand how water, plants, geology, and climate interact in a tightly coupled system, and how humans are changing this system. The research focuses on areas such as climate change and water security, eco-informatics, ecosystems and disturbance, and urban systems. Professor Naomi Tague has been recognized for her achievements in research, including receiving an American Geophysical Union Fellowship Award in 2024 for her work in advancing understanding and prediction in her field. The lab's work includes developing models and approaches to better understand dynamic water storage, the co-evolution of trees and snowpack following disturbances, and the impacts of climate and geology on stream characteristics. The lab also explores innovative methods such as the Virtual Hydrological Laboratory approach and investigates issues related to nitrogen deposition and export in dryland watersheds. Professor Tague actively shares her expertise through presentations, lectures, and public exhibits, contributing significantly to ecohydrologic modeling and water resource management.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Algorithm
• Human–computer interaction
• Psychology
• Physics
• Multimedia
• Visual arts
• Aesthetics
• Social psychology

Selected publications

• Clay ARTools: Precise Machine Toolpath Editing for Clay 3D Printing With Craft-Inspired Direct Manipulation Tools in AR

  2026-04-13 · 1 citations

  articleOpen access

  Ceramics practice is an embodied activity where creators use manual tools in unique ways to shape physical material. Clay 3D printing uses the same material as manual ceramics craft, enabling new opportunities for form and texture by precisely controlling the 3D printing toolpath. However, current clay 3D printing design workflows require developing forms through digital software rather than tool-based making. We present Clay ARtools, an augmented reality (AR) system for designing clay 3D printed vessels. We developed Clay ARtools in collaboration with a professional ceramicist to create AR toolpath editing operations that reference manual use of ceramic tools. Through the design and fabrication of 3D-printed clay artifacts, we demonstrate how AR ceramic tools enable precise and controllable modifications of the toolpath, from the overall form down to individual toolpath points. We demonstrate how extending physical tool metaphors with digital representations and numerical precision enables craft-like interaction with CAM-based design techniques.

  PublisherDOI

• SABER: Spatial Attention, Brain, Extended Reality

  ArXiv.org · 2026-03-25

  articleOpen access

  Tracking moving objects is a critical skill for many everyday tasks, such as crossing a busy street, driving a car or catching a ball. Attention is a key cognitive function that supports object tracking; however, our understanding of the brain mechanisms that support attention is almost exclusively based on evidence from tasks that present stable objects at fixed locations. Accounts of multiple object tracking are also limited because they are largely based on behavioral data alone and involve tracking objects in a 2D plane. Consequently, the neural mechanisms that enable moment-by-moment tracking of goal-relevant objects remain poorly understood. To address this knowledge gap, we developed SABER (Spatial Attention, Brain, Extended Reality), a new framework for studying the behavioral and neural dynamics of attention to objects moving in 3D. Participants (n=32) completed variants of a task inspired by the popular virtual reality (VR) game, Beat Saber, where they used virtual sabers to strike stationary and moving color-defined target spheres while we recorded electroencephalography (EEG). We first established that standard univariate EEG metrics which are typically used to study spatial attention to static objects presented on 2D screens, can generalize effectively to an immersive VR context involving both static and dynamic 3D stimuli. We then used a computational modeling approach to reconstruct moment-by-moment attention to the locations of stationary and moving objects from oscillatory brain activity, demonstrating the feasibility of precisely tracking attention in a 3D space. These results validate SABER, and provide a foundation for future research that is critical not only for understanding how attention works in the physical world, but is also directly relevant to the development of better VR applications.

  PublisherOA PDF

• How Users Perceive Mixed-Initiative AI: Attitudes Toward Assistance in Problem Solving

  Open MIND · 2026-02-01

  preprint

  In mixed-initiative systems, the mode of AI assistance delivery can be as consequential as the assistance itself. We investigated two assistance delivery modes: on-demand help (users request via Button) and pre-scheduled help (assistance delivered at user-selected intervals, with user actions resetting the Timer). To evaluate these modes, we selected Rush Hour puzzles as the human-AI collaborative task because they capture elements of real-world problem solving such as analysis, resource management, and decision-making under constraints. To enhance ecological validity, we imposed monetary costs for both time and AI assistance, simulating scenarios where people must balance implicit or explicit trade-offs such as time pressure, financial limitations, or opportunity costs. Although task performance was comparable across modes, participants who used the pre-scheduled (Timer) mode reported more positive perceptions of the AI, even when their ending budget was low. This suggests that assistance delivery mode can shape user experience independent of task outcomes, indicating that human-AI systems may need to consider how AI assistance is delivered alongside improving task performance.

  DOI

• How Users Perceive Mixed-Initiative AI: Attitudes Toward Assistance in Problem Solving

  2026-03-03 · 1 citations

  articleOpen access

  In mixed-initiative systems, the mode of AI assistance delivery can be as consequential as the assistance itself. We investigated two assistance delivery modes: on-demand help (users request via Button) and pre-scheduled help (assistance delivered at user-selected intervals, with user actions resetting the Timer). To evaluate these modes, we selected Rush Hour puzzles as the human–AI collaborative task because they capture elements of real-world problem solving such as analysis, resource management, and decision-making under constraints. To enhance ecological validity, we imposed monetary costs for both time and AI assistance, simulating scenarios where people must balance implicit or explicit trade-offs such as time pressure, financial limitations, or opportunity costs. Although task performance was comparable across modes, participants who used the pre-scheduled (Timer) mode reported more positive perceptions of the AI, even when their ending budget was low. This suggests that assistance delivery mode can shape user experience independent of task outcomes, indicating that human-AI systems may need to consider how AI assistance is delivered alongside improving task performance.

  PublisherDOI

• Embedded vs. Situated: An Evaluation of AR Facial Training Feedback

  ArXiv.org · 2026-02-01

  articleOpen access

  While augmented reality (AR) research demonstrates benefits of embedded visualizations for gross motor training, its applicability to facial exercises remains under-explored. Providing effective real-time feedback for facial muscle training presents unique design challenges, given the complexity of facial musculature. We developed three AR feedback approaches varying in spatial relationship to the user: situated (screen-fixed), proxy-embedded (on a mannequin), and fully embedded (overlaid on the user's face). In a within-subjects study (N=24), we measured exercise accuracy, cognitive load, and user preference during facial training tasks. The embedded feedback reduced cognitive load and received higher preference ratings, while the situated feedback enabled more precise corrections and higher accuracy. Qualitative analysis revealed a key design tension: embedded feedback improved experience but created self-consciousness and interpretive difficulty. We distill these insights into design considerations addressing the trade-offs for facial training systems, with implications for rehabilitation, performance training, and motor skill acquisition.

  PublisherOA PDF

• Embedded vs. Situated: An Evaluation of AR Facial Training Feedback

  Open MIND · 2026-02-01

  preprint

  While augmented reality (AR) research demonstrates benefits of embedded visualizations for gross motor training, its applicability to facial exercises remains under-explored. Providing effective real-time feedback for facial muscle training presents unique design challenges, given the complexity of facial musculature. We developed three AR feedback approaches varying in spatial relationship to the user: situated (screen-fixed), proxy-embedded (on a mannequin), and fully embedded (overlaid on the user's face). In a within-subjects study (N=24), we measured exercise accuracy, cognitive load, and user preference during facial training tasks. The embedded feedback reduced cognitive load and received higher preference ratings, while the situated feedback enabled more precise corrections and higher accuracy. Qualitative analysis revealed a key design tension: embedded feedback improved experience but created self-consciousness and interpretive difficulty. We distill these insights into design considerations addressing the trade-offs for facial training systems, with implications for rehabilitation, performance training, and motor skill acquisition.

  DOI

• SABER: Spatial Attention, Brain, Extended Reality

  2026-03-21

  article

  Tracking moving objects is a critical skill for many everyday tasks, such as crossing a busy street, driving a car or catching a ball. Attention is a key cognitive function that supports object tracking; however, our understanding of the brain mechanisms that support attention is almost exclusively based on evidence from tasks that present stable objects at fixed locations. Accounts of multiple object tracking are also limited because they are largely based on behavioral data alone and involve tracking objects in a 2D plane. Consequently, the neural mechanisms that enable moment-by-moment tracking of goal-relevant objects remain poorly understood. To address this knowledge gap, we developed SABER (Spatial Attention, Brain, Extended Reality), a new framework for studying the behavioral and neural dynamics of attention to objects moving in 3D. Participants (n=32) completed variants of a task inspired by the popular virtual reality (VR) game Beat Saber, where they used virtual sabers to strike stationary and moving color-defined target spheres while we recorded electroencephalography (EEG). We first established that standard univariate EEG metrics which are typically used to study spatial attention to static objects presented on 2D screens, can generalize effectively to an immersive VR context involving both static and dynamic 3D stimuli. We then used a computational modeling approach to reconstruct moment-by-moment attention to the locations of stationary and moving objects from oscillatory brain activity, demonstrating the feasibility of precisely tracking attention in a 3D space. These results validate SABER, and provide a foundation for future research that is critical not only for understanding how attention works in the physical world, but is also directly relevant to the development of better VR applications. The insights gained here can potentially inform the design of more intuitive interfaces, effective training simulations, and immersive experiences optimized for the human attention system.

  PublisherDOI

• SABER: Spatial Attention, Brain, Extended Reality

  arXiv (Cornell University) · 2026-03-25

  preprintOpen access

  Tracking moving objects is a critical skill for many everyday tasks, such as crossing a busy street, driving a car or catching a ball. Attention is a key cognitive function that supports object tracking; however, our understanding of the brain mechanisms that support attention is almost exclusively based on evidence from tasks that present stable objects at fixed locations. Accounts of multiple object tracking are also limited because they are largely based on behavioral data alone and involve tracking objects in a 2D plane. Consequently, the neural mechanisms that enable moment-by-moment tracking of goal-relevant objects remain poorly understood. To address this knowledge gap, we developed SABER (Spatial Attention, Brain, Extended Reality), a new framework for studying the behavioral and neural dynamics of attention to objects moving in 3D. Participants (n=32) completed variants of a task inspired by the popular virtual reality (VR) game, Beat Saber, where they used virtual sabers to strike stationary and moving color-defined target spheres while we recorded electroencephalography (EEG). We first established that standard univariate EEG metrics which are typically used to study spatial attention to static objects presented on 2D screens, can generalize effectively to an immersive VR context involving both static and dynamic 3D stimuli. We then used a computational modeling approach to reconstruct moment-by-moment attention to the locations of stationary and moving objects from oscillatory brain activity, demonstrating the feasibility of precisely tracking attention in a 3D space. These results validate SABER, and provide a foundation for future research that is critical not only for understanding how attention works in the physical world, but is also directly relevant to the development of better VR applications.

  PublisherDOI

• Embedded vs. Situated: An Evaluation of AR Facial Training Feedback

  2026-04-13 · 1 citations

  articleOpen access

  While augmented reality (AR) research demonstrates benefits of embedded visualizations for gross motor training, its applicability to facial exercises remains under-explored. Providing effective real-time feedback for facial muscle training presents unique design challenges, given the complexity of facial musculature. We developed three AR feedback approaches varying in spatial relationship to the user: situated (screen-fixed), proxy-embedded (on a mannequin), and fully embedded (overlaid on the user’s face). In a within-subjects study (N=24), we measured exercise accuracy, cognitive load, and user preference during facial training tasks. The embedded feedback reduced cognitive load and received higher preference ratings, while the situated feedback enabled more precise corrections and higher accuracy. Qualitative analysis revealed a key design tension: embedded feedback improved experience but created self-consciousness and interpretive difficulty. We distill these insights into design considerations addressing the trade-offs for facial training systems, with implications for rehabilitation, performance training, and motor skill acquisition.

  PublisherDOI

• Beyond Physical Reach: Comparing Head- and Cane-Mounted Cameras for Last-Mile Navigation by Blind Users

  ArXiv.org · 2025-04-27

  preprintOpen access

  Blind individuals face persistent challenges in last-mile navigation, including locating entrances, identifying obstacles, and navigating complex or cluttered spaces. Although wearable cameras are increasingly used in assistive systems, there has been no systematic, vantage-focused comparison to guide their design. This paper addresses that gap through a two-part investigation. First, we surveyed ten experienced blind cane users, uncovering navigation strategies, pain points, and technology preferences. Participants stressed the importance of multi-sensory integration, destination-focused travel, and assistive tools that complement (rather than replace) the cane's tactile utility. Second, we conducted controlled data collection with a blind participant navigating five real-world environments using synchronized head- and cane-mounted cameras, isolating vantage placement as the primary variable. To assess how each vantage supports spatial perception, we evaluated SLAM performance (for localization and mapping) and NeRF-based 3D reconstruction (for downstream scene understanding). Head-mounted sensors delivered superior localization accuracy, while cane-mounted views offered broader ground-level coverage and richer environmental reconstructions. A combined (head+cane) configuration consistently outperformed both. These results highlight the complementary strengths of different sensor placements and offer actionable guidance for developing hybrid navigation aids that are perceptive, robust, and user-aligned.

  PublisherOA PDFDOI

Recent grants

• EAGER: Large-Scale Real-Time Information Visualization on Immersive Platforms

  NSF · $93k · 2017–2019

• Collaborative Research: HCC: Medium: HCI in Motion -- Using EEG, Eye Tracking, and Body Sensing for Attention-Aware Mobile Mixed Reality

  NSF · $739k · 2022–2026

• CHS: Small: Integrative Wide-Area Augmented Reality Scene Modeling

  NSF · $500k · 2019–2023

• EAGER: Collaborative Visualization for Knowledge Computing

  NSF · $135k · 2010–2013

• Scalable Visualization and Constrained Interaction for Large Graphs -- Supporting the Collaborative Analysis of High-dimensional Data Sets

  NSF · $493k · 2006–2010

Frequent coauthors

• John O’Donovan

  University of California, Santa Barbara

  35 shared

• Stephen DiVerdi

  Adobe Systems (United States)

  28 shared

• Hirokazu Kato

  Nara Institute of Science and Technology

  28 shared

• Mark Billinghurst

  University of South Australia

  27 shared

• Steve Feiner

  University of South Australia

  25 shared

• Steve Woods

  Georgia Institute of Technology

  25 shared

• Cristina Ceballos

  Institute of Electrical and Electronics Engineers

  25 shared

• Omar Niamut

  Saarland University

  25 shared

Labs

• Tobias Hollerer's LabPI

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Awards & honors

• John Simon Guggenheim Fellowship in Visual Arts (2016)
• Making Visible the Invisible (permanent installation at Seat…
• Creative Capital Foundation support
• Daniel Langlois Foundation for the Arts, Science and Technol…
• Canada Council for the Arts support