Person:
Arriaga, Rosa I.

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    Forms of Accountability at the Intersection of Science and Design: Implications from Ecologies of Care Studies in PTSD and Diabetes
    (Georgia Institute of Technology, 2022-10-20) Arriaga, Rosa I.
    Computing holds the promise of alleviating negative impacts of mental illness and chronic disorders by scaling human effort and best medical practices over time and space. One in five adults is experiencing mental illness and four in ten adults in the US have two or more chronic diseases. The urgent need to manage these conditions calls for robust, and reliable technology that is useful and usable by patients and their caregivers. It calls for accountability at the intersection of science and design. In this talk, I will demonstrate how human-centered computing can leverage the generalizability of theoretical frameworks to design and build computational systems for Post-Traumatic Stress Disorder (PTSD) and Diabetes. I will discuss unique challenges in each clinical domain and will present theory-driven technology interventions that address them. I will also explore how these interventions can lead to improved health and wellness in diverse populations.
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    Tools for Measuring and Understanding the Proximity of Users to Their Smartphones
    (Georgia Institute of Technology, 2020-03) Park, Jung Wook ; Evans, Hayley I. ; Watson, Hue L. ; Abowd, Gregory D. ; Arriaga, Rosa I.
    Two studies in ubiquitous computing examined the proximity of users to their smartphones in 2006 and in 2011. Both studies have used a passive data collection tool and the day reconstruction method. Additionally, Dey at al. adopted an online survey to validate their findings with a larger population sample. In 2019, we attempted to revisit this research topic due to the high adoption rate of smartphone and smart- watch. In our replication study, we developed a new passive data collection tool and a novel survey technique, proximity-based ecological momentary assessments. We also adopted the day reconstruction method and online survey utilized in the previous studies. This technical report presents the details of the research tools and techniques used in our study. This technical report is a supplementary material to the published article, "Growing Apart: How SmartDevices Impact the Proximity of Users to Their Smartphones", in IEEE Pervasive Computing.
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    Exploiting social partners in robot learning
    (Georgia Institute of Technology, 2010) Cakmak, Maya ; DePalma, Nick ; Arriaga, Rosa I. ; Thomaz, Andrea L.
    Social learning in robotics has largely focused on imitation learning. Here we take a broader view and are interested in the multifaceted ways that a social partner can in uence the learning process. We implement four social learning mechanisms on a robot: stimulus enhancement, emulation, mimicking, and imitation, and illustrate the computational benefits of each. In particular, we illustrate that some strategies are about directing the attention of the learner to objects and others are about actions. Taken together these strategies form a rich repertoire allowing social learners to use a social partner to greatly impact their learning process. We demonstrate these results in simulation and with physical robot `playmates'.
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    Effects of Social Exploration Mechanisms on Robot Learning
    (Georgia Institute of Technology, 2009) Cakmak, Maya ; DePalma, Nick ; Thomaz, Andrea L. ; Arriaga, Rosa I.
    Social learning in robotics has largely focused on imitation learning. Here we take a broader view and are interested in the multifaceted ways that a social partner can influence the learning process. We implement four social learning mechanisms on a robot: stimulus enhancement, emulation, mimicking, and imitation, and illustrate the computational benefits of each. In particular, we illustrate that some strategies are about directing the attention of the learner to objects and others are about actions. Taken together these strategies form a rich repertoire allowing social learners to use a social partner to greatly impact their learning process. We demonstrate these results in simulation and with physical robot ‘playmates’.
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    Computational Benefits of Social Learning Mechanisms: Stimulus Enhancement and Emulation
    (Georgia Institute of Technology, 2009) Cakmak, Maya ; DePalma, Nick ; Arriaga, Rosa I. ; Thomaz, Andrea L.
    Social learning in robotics has largely focused on imitation learning. In this work, we take a broader view of social learning and are interested in the multifaceted ways that a social partner can influence the learning process. We implement stimulus enhancement and emulation on a robot, and illustrate the computational benefits of social learning over individual learning. Additionally we characterize the differences between these two social learning strategies, showing that the preferred strategy is dependent on the current behavior of the social partner. We demonstrate these learning results both in simulation and with physical robot ‘playmates’.