Scientists 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.

The Mars Perseverance Rover represents a generational change in the scale of measurements that can be taken on Mars, however this increased resolution introduces new challenges for techniques in exploratory data analysis. The multiple different instruments on the rover each measures specific properties of interest to scientists, so analyzing how underlying phenomena affect multiple different instruments together is important to understand the full picture. However each instrument has a unique resolution, making the mapping between overlapping layers of data non-trivial. In this work, we introduce Nested Fusion, a method to combine arbitrarily layered datasets of different resolutions and produce a latent distribution at the highest possible resolution, encoding complex interrelationships between different measurements and scales. Our method is efficient for large datasets, can perform inference even on unseen data, and outperforms existing methods of dimensionality reduction and latent analysis on real-world Mars rover data. We have deployed our method Nested Fusion within a Mars science team at NASA Jet Propulsion Laboratory (JPL) and through multiple rounds of participatory design enabled greatly enhanced exploratory analysis workflows for real scientists. To ensure the reproducibility of our work we have open sourced our code on GitHub at https://github.com/pixlise/NestedFusion.

Reconstruction of 3D scenes from 2D images is a technical challenge that impacts domains from Earth and planetary sciences and space exploration to augmented and virtual reality. Typically, reconstruction algorithms first identify common features across images and then minimize reconstruction errors after estimating the shape of the terrain. This bundle adjustment (BA) step optimizes around a single, simplifying scalar value that obfuscates many possible causes of reconstruction errors (e.g., initial estimate of the position and orientation of the camera, lighting conditions, ease of feature detection in the terrain). Reconstruction errors can lead to inaccurate scientific inferences or endanger a spacecraft exploring a remote environment. To address this challenge, we present VECTOR, a visual analysis tool that improves error inspection for stereo reconstruction BA. VECTOR provides analysts with previously unavailable visibility into feature locations, camera pose, and computed 3D points. VECTOR was developed in partnership with the Perseverance Mars Rover and Ingenuity Mars Helicopter terrain reconstruction team at the NASA Jet Propulsion Laboratory. We report on how this tool was used to debug and improve terrain reconstruction for the Mars 2020 mission.

While anomaly detection stands among the most important and valuable problems across many scientific domains, anomaly detection research often focuses on AI methods that can lack the nuance and interpretability so critical to conducting scientific inquiry. We believe this exclusive focus on algorithms with a fixed framing ultimately blocks scientists from adopting even high-accuracy anomaly detection models in many scientific use cases. In this application paper we present the results of utilizing an alternative approach that situates the mathematical framing of machine learning based anomaly detection within a participatory design framework. In a collaboration with NASA scientists working with the PIXL instrument studying Martian planetary geochemistry as a part of the search for extra-terrestrial life; we report on over 18 months of in-context user research and co-design to define the key problems NASA scientists face when looking to detect and interpret spectral anomalies. We address these problems and develop a novel spectral anomaly detection toolkit for PIXL scientists that is highly accurate (93.4% test accuracy on detecting diffraction anomalies), while maintaining strong transparency to scientific interpretation. We also describe outcomes from a yearlong field deployment of the algorithm and associated interface, now used daily as a core component of the PIXL science team’s workflow, and directly situate the algorithm as a key contributor to discoveries around the potential habitability of Mars. Finally we introduce a new design framework which we developed through the course of this collaboration for co-creating anomaly detection algorithms: Iterative Semantic Heuristic Modeling of Anomalous Phenomena (ISHMAP), which provides a process for scientists and researchers to produce natively interpretable anomaly detection models. This work showcases an example of successfully bridging methodologies from AI and HCI within a scientific domain, and provides a resource in ISHMAP which may be used by other researchers and practitioners looking to partner with other scientific teams to achieve better science through more effective and interpretable anomaly detection tools.

The 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.

Operations 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.

Effective data visualization is a key part of the discovery process in the era of “big data”. It is the bridge between the quantitative content of the data and human intuition, and thus an essential component of the scientific path from data into knowledge and understanding. Visualization is also essential in the data mining process, directing the choice of the applicable algorithms, and in helping to identify and remove bad data from the analysis. However, a high complexity or a high dimensionality of modern data sets represents a critical obstacle. How do we visualize interesting structures and patterns that may exist in hyper-dimensional data spaces? A better understanding of how we can perceive and interact with multidimensional information poses some deep questions in the field of cognition technology and human-computer interaction. To this effect, we are exploring the use of immersive virtual reality platforms for scientific data visualization, both as software and inexpensive commodity hardware. These potentially powerful and innovative tools for multi-dimensional data visualization can also provide an easy and natural path to a collaborative data visualization and exploration, where scientists can interact with their data and their colleagues in the same visual space. Immersion provides benefits beyond the traditional “desktop” visualization tools: it leads to a demonstrably better perception of a datascape geometry, more intuitive data understanding, and a better retention of the perceived relationships in the data.

Many tasks call for efficient user interaction under time delay-controlling space instruments, piloting remote aircraft and operating search and rescue robots. In this paper we identify an underexplored design opportunity for building robotic teleoperation user interfaces following an evaluation of operator performance during a time-delayed robotic arm block-stacking task in twenty-two participants. More delay resulted in greater operator hesitation and a decreased ratio of active to inactive input. This ratio can serve as a useful proxy for measuring an operator's ability to anticipate the outcome of their control inputs before receiving delayed visual feedback. High anticipatory input ratio (AIR) scores indicate times when robot operators enter commands before waiting for visual feedback. Low AIR scores highlight when operators must wait for visual feedback before continuing. We used this measurement to help us identify particular sub-tasks where operators would likely benefit from additional support.

NASA's Telexploration Project seeks to make us better explorers by building immersive environments that feel like we are really there. The Mission Operations Innovation Office and its Operations Laboratory at the NASA Jet Propulsion Laboratory (JPL) founded the Telexploration Project, and is researching how immersive visualization and natural human-robot interaction can enable mission scientists, engineers, and the general public to interact with NASA spacecraft and alien environments in a more effective way. These efforts have been accelerated through partnerships with many different companies, especially in the video game industry. These demos will exhibit some of the progress made at NASA and its commercial partnerships by allowing attendees to experience Mars data acquired from NASA spacecraft in a head mounted display using several rendering and interaction techniques.

Designing radically new technology systems that people will want to use is complex. Design teams must draw on knowledge related to people's current values and desires to envision a preferred yet plausible future. However, the introduction of new technology can shape people's values and practices, and what-we-know-now about them does not always translate to an effective guess of what the future could, or should, be. New products and systems typically exist outside of current understandings of technology and use paradigms; they often have few interaction and social conventions to guide the design process, making efforts to pursue them complex and risky. User Enactments (UEs) have been developed as a design approach that aids design teams in more successfully investigate radical alterations to technologies' roles, forms, and behaviors in uncharted design spaces. In this paper, we reflect on our repeated use of UE over the past five years to unpack lessons learned and further specify how and when to use it. We conclude with a reflection on how UE can function as a boundary object and implications for future work.

Part of being a parent is taking responsibility for arranging and supplying transportation of children between various events. Dual-income parents frequently develop routines to help manage transportation with a minimal amount of attention. On days when families deviate from their routines, effective logistics can often depend on knowledge of the routine location, availability and intentions of other family members. Since most families rarely document their routine activities, making that needed information unavailable, coordination breakdowns are much more likely to occur. To address this problem we demonstrate the feasibility of learning family routines using mobile phone GPS. We describe how we (1) detect pick-ups and drop-offs; (2) predict which parent will perform a future pick-up or drop-off; and (3) infer if a child will be left at an activity. We discuss how these routine models give digital calendars, reminder and location systems new capabilities to help prevent breakdowns, and improve family life.

The complexities and costs of deploying Ubicomp applications seriously compromise our ability to evaluate such systems in the real world. To simplify Ubicomp deployment we introduce the robotic pseudopod (P.Pod), an actuator that acts on mechanical switches originally designed for human control only. P.Pods enable computational control of devices by hijacking their mechanical switches -- a term we refer to as mechanical hijacking. P.Pods offer simple, low-cost, non-destructive computational access to installed hardware, enabling functional, real world Ubicomp deployments. In this paper, we illustrate how three P.Pod primitives, built with the Lego MindStorm NXT toolkit, can implement mechanical hijacking, facilitating real world Ubicomp deployments which otherwise require extensive changes to existing hardware or infrastructure. Lastly, we demonstrate the simplicity of P.Pods by observing two middle school classes build working smart home applications in 4 hours.

Researchers have detailed the importance of routines in how people live and work, while also cautioning system designers about the importance of people's idiosyncratic behavior patterns and the challenges they would present to learning systems. We wish to take up their challenge, and offer a vision of how simple sensing technology could capture and model idiosyncratic routines, enabling applications to solve many real world problems.