DeepSee: Multidimensional Visualizations of Seabed EcosystemsScientists studying deep ocean microbial ecosystems use limited numbers of sediment samples collected from the seafloor to characterize important life-sustaining biogeochemical cycles in the environment. Yet conducting fieldwork to sample these extreme remote environments is both expensive and time consuming, requiring tools that enable scientists to explore the sampling history of field sites and predict where taking new samples is likely to maximize scientific return. We conducted a collaborative, user-centered design study with a team of scientific researchers to develop DeepSee, an interactive data workspace that visualizes 2D and 3D interpolations of biogeochemical and microbial processes in context together with sediment sampling history overlaid on 2D seafloor maps. Based on a field deployment and qualitative interviews, we found that DeepSee increased the scientific return from limited sample sizes, catalyzed new research workflows, reduced long-term costs of sharing data, and supported teamwork and communication between team members with diverse research goals.2024ACAdam J Coscia et al.Georgia Institute of TechnologyGeospatial & Map VisualizationMedical & Scientific Data VisualizationCHI
From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal RecommendationsSelf-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants’ self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.2021EMElliot G. Mitchell et al.Columbia UniversityAI-Assisted Decision-Making & AutomationChronic Disease Self-Management (Diabetes, Hypertension, etc.)CHI
Preserving Agency During Electrical Muscle Stimulation Training Speeds up Reaction Time Directly After Removing EMSForce feedback devices, such as motor-based exoskeletons or wearables based on electrical muscle stimulation (EMS), have the unique potential to accelerate users’ own reaction time (RT). However, this speedup has only been explored while the device is attached to the user. In fact, very little is known regarding whether this faster reaction time still occurs after the user removes the device from their bodies–this is precisely what we investigated by means of a simple reaction time (RT) experiment, in which participants were asked to tap as soon as they saw an LED flashing. Participants experienced this in three EMS conditions: (1) fast-EMS, the electrical impulses were synced with the LED; (2) agency-EMS, the electrical impulse was delivered 40ms faster than the participant’s own RT, which prior work has shown to preserve one’s sense of agency over this movement; and, (3) late-EMS: the impulse was delivered after the participant’s own RT. Our results revealed that the participants’ RT was significantly reduced by approximately 8ms(up to 20ms) only after training with the agency-EMS condition. This finding suggests that the prioritizing agency during EMS training is key to motor-adaptation, i.e., it enables a faster motor response even after the user has removed the EMS device from their body.2021SKShunichi Kasahara et al.Sony CSL, The University of TokyoVibrotactile Feedback & Skin StimulationElectrical Muscle Stimulation (EMS)CHI
Trust in Collaborative Automation in High Stakes Software Engineering Work: A Case Study at NASAThe amount of autonomy in software engineering tools is increasing as developers build increasingly complex systems. We study factors influencing software engineers’ trust in an autonomous tool situated in a high stakes workplace, because research in other contexts shows that too much or too little trust in autonomous tools can have negative consequences. We present the results of a ten week ethnographic case study of engineers collaborating with an autonomous tool to write control software at the National Aeronautics and Space Administration to support high stakes missions. We find that trust in an autonomous software engineering tool in this setting was influenced by four main factors: the tool’s transparency, usability, its social context, and the organization’s associated processes. Our observations lead us to frame trust as a quality the operator places in their collaboration with the automated system, and we outline implications of this framing and other results for researchers studying trust in autonomous systems, designers of software engineering tools, and organizations conducting high stakes work with these tools.2021DWDavid Gray Widder et al.Carnegie Mellon UniversityExplainable AI (XAI)AI-Assisted Decision-Making & AutomationAlgorithmic Transparency & AuditabilityCHI
Personal Health Oracle: Explorations of Personalized Predictions in Diabetes Self-ManagementThe increasing availability of health data and knowledge about computationally modeling human physiology opens new opportunities for personalized predictions in health. Yet little is known about how individuals interact and reason with personalized predictions. To explore these questions, we developed a smartphone app, GlucOracle, that uses self-tracking data of individuals with type 2 diabetes to generate personalized forecasts for post-meal blood glucose levels. We pilot-tested GlucOracle with two populations: members of an online diabetes community, knowledgeable about diabetes and technologically savvy; and individuals from a low socio-economic status community, characterized by high prevalence of diabetes, low literacy and limited experience with mobile apps. Individuals in both communities engaged with personal glucose forecasts and found them useful for adjusting immediate meal options, and planning future meals. However, the study raised new questions as to appropriate time, form, and focus of forecasts and suggested new research directions for personalized predictions in health.2019PDPooja M. Desai et al.Columbia University Teachers CollegeAI-Assisted Decision-Making & AutomationChronic Disease Self-Management (Diabetes, Hypertension, etc.)Biosensors & Physiological MonitoringCHI
Towards Design Principles for Visual Analytics in Operations ContextsOperations engineering teams interact with complex data systems to make technical decisions that ensure the operational efficacy of their missions. To support these decision-making tasks, which may require elastic prioritization of goals dependent on changing conditions, custom analytics tools are often developed. We were asked to develop such a tool by a team at the NASA Jet Propulsion Laboratory, where rover telecom operators make decisions based on models predicting how much data rovers can transfer from the surface of Mars. Through research, design, implementation, and informal evaluation of our new tool, we developed principles to inform the design of visual analytics systems in operations contexts. We offer these principles as a step towards understanding the complex task of designing these systems. The principles we present are applicable to designers and developers tasked with building analytics systems in domains that face complex operations challenges such as scheduling, routing, and logistics.2018MCMatthew Conlen et al.University of WashingtonInteractive Data VisualizationGeospatial & Map VisualizationPrototyping & User TestingCHI