Laura Barnes

About

Laura Barnes is a professor in the Department of Systems and Information Engineering at the University of Virginia. She serves as the Associate Director of the Link Lab, a multidisciplinary center focused on research and education in Cyber-Physical Systems. Professor Barnes directs the Sensing Systems for Health Lab, which is dedicated to designing impactful, technology-enabled solutions aimed at improving health and well-being. Her work integrates sensing technologies with health applications to create innovative approaches that enhance quality of life.

Research topics

• Artificial Intelligence
• Psychology
• Computer Science
• Machine Learning
• Medicine
• Clinical psychology
• Gerontology
• Psychiatry
• Social psychology

Selected publications

• The Urban Built Environment, Walking and Mental Health Outcomes Among Older Adults: A Pilot Study

  Frontiers in Public Health · 2020 · 128 citations

  < 0.05), accompanied by faster cognitive reaction times post-walk, albeit not statistically significant in this small sample. Cognitive recall of the route varied between urban gray and urban green conditions, as participants were more likely to rely on natural features to define their routes when present. The environmental and physiologic data sets were converged to show a significant effect of ambient noise and urban conditions on stress activation as measured by heart rate variability. Findin…

  DOI

• MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning

  2021 IEEE/CVF International Conference on Computer Vision (ICCV) · 2021 · 54 citations

  Senior authorCorresponding

  Inspired by human visual attention, we propose a novel inverse reinforcement learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for predicting the visual attention of drivers in accident-prone situations. MEDIRL predicts fixation locations that lead to maximal rewards by learning a task-sensitive reward function from eye fixation patterns recorded from attentive drivers. Additionally, we introduce EyeCar, a new driver attention dataset in accident-prone situa…

  DOI

• PALLM: Evaluating and Enhancing <u>PALL</u> iative Care Conversations with <u>L</u> arge <u>L</u> anguage <u>M</u> odels

  ACM Transactions on Computing for Healthcare · 2025-01-23 · 4 citations

  articleSenior author

  Effective patient-provider communication is crucial in clinical care, directly impacting patient outcomes and quality of life. Traditional evaluation methods, such as human ratings, patient feedback, and provider self-assessments, are often limited by high costs and scalability issues. Although existing natural language processing (NLP) techniques show promise, they struggle with the nuances of clinical communication and require sensitive clinical data for training, reducing their effectiveness…

  PublisherDOI

• Wearable Sensor-Based Multimodal Physiological Responses of Socially Anxious Individuals in Social Contexts on Zoom

  IEEE Transactions on Affective Computing · 2025-04-21 · 3 citations

  articleOpen accessSenior author

  Correctly identifying an individual's social context from passively worn sensors holds promise for delivering just-in-time adaptive interventions (JITAIs) to treat social anxiety. In this study, we present results using passively collected data from a within-subjects experiment that assessed physiological responses across different social contexts (i.e., alone vs. with others), social phases (i.e., pre- and post-interaction vs. during an interaction), social interaction sizes (i.e., dyadic vs. g…

  PublisherOA PDFDOI

• Understanding State Social Anxiety in Virtual Social Interactions Using Multimodal Wearable Sensing Indicators

  2025-06-16 · 3 citations

  articleSenior author

  Mobile sensing is ubiquitous and offers opportunities to gain insight into state mental health functioning. Detecting state elevations in social anxiety would be especially useful given this phenomenon is highly prevalent and impairing, but often not disclosed. In the present work, we explore the feasibility of detecting fluctuations in state social anxiety among N = 46 undergraduate students with elevated symptoms of trait social anxiety. Participants engaged in two dyadic and two group social…

  PublisherDOI

Recent grants

• SCH:INT: Collaborative Research: Multiscale Modeling and Intervention for Improving Long-Term Medication

  NIH · $1.1M · 2019–2025

• SCH: INT: Context-Aware Micro-Interventions for Social Anxiety

  NIH · $1.3M · 2022–2027

• Feasibility of Virtual Agent Cervical Cancer Education for Hispanic Farmworkers

  NIH · $374k · 2012–2017

Frequent coauthors

• Mehdi Boukhechba

  Janssen (United States)

  167 shared

• Bethany A. Teachman

  75 shared

• Haoyi Xiong

  42 shared

• Lihua Cai

  South China Normal University

  38 shared

• Zhiyuan Wang

  Shenyang Aerospace University

  31 shared

• Sijia Yang

  North China University of Technology

  27 shared

• Daqing Zhang

  Peking University

  27 shared

• Matthew S. Gerber

  University of Virginia

  27 shared

Labs

• Link LabPI

Similar researchers at University of Virginia

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• Roman Krzysztofowiczh-30
• Gregory J. Gerlingh-25