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We present Discovery Dashboard, a visual analytics system for exploring large volumes of time series data from mobile medical field studies. Discovery Dashboard offers interactive exploration tools and a data mining motif discovery algorithm to help researchers formulate hypotheses, discover trends and patterns, and ultimately gain a deeper understanding of their data. Discovery Dashboard emphasizes user freedom and flexibility during the data exploration process and enables researchers to do things previously challenging or impossible to do --- in the web-browser and in real time. We demonstrate our system visualizing data from a mobile sensor study conducted at the University of Minnesota that included 52 participants who were trying to quit smoking.

This article develops a method for investigating the consequences of algorithmic systems according to the documents that specify their design constrains. As opposed to reverse engineering algorithms to identify how their logic operates, the article proposes to design or "forward engineer" algorithmic systems in order to theorize how their consequences are informed by design constraints: the specific problems, use cases, and presuppositions that they respond to. This demands a departure from algorithmic reformism, which responds to concerns about the consequences of algorithmic systems by proposing to make algorithms more transparent or less biased. Instead, by investigating algorithmic systems according to documents that specify their design constraints, we identify how the consequences of algorithms are presupposed by the problems that they propose to solve, the types of solutions that they enlist to solve these problems, and the systems of authority that these solutions depend on. To accomplish this, this article develops a methodological framework for researching the process of designing algorithmic systems. In doing so, it proposes to move beyond reforming the technical implementation details of algorithms in order to address the design problems and constraints that underlie them.

With every advancement in mHealth sensing technology, we are presented with an abundance of data streams and models that enable us to make sense of health information we record. To distill this diverse and ever-growing data into meaningful information, we must first develop tools that can represent data intuitively and are flexible enough to handle the special characteristics of mHealth records. For example, whereas traditional health data such as electronic health records (EHR) often consist of discrete events that may be readily analyzed and visualized, mHealth entails sensor ensembles that generate continuous, multivariate data streams of high-resolution and often noisy measurements. Drawing from methodologies in machine learning and visualization, interactive visual analytics tools are an increasingly important aid to making sense of this complexity. Still, these computational and visual techniques must be employed attentively to represent this data not only intuitively, but also accurately, transparently, and in a way that is driven by user needs. Acknowledging these challenges, we review existing visual analytic tools to identify design solutions that are both useful for and adaptable to the demands of mHealth data analysis tasks. In doing so, we identify open problems for representing and understanding mHealth data, suggesting future research directions for developers in the field.

Whereas event-based timelines for healthcare enable users to visualize the chronology of events surrounding events of interest, they are often not designed to aid the discovery, construction, or comparison of associated cohorts. We present TimeStitch, a system that helps health researchers discover and understand events that may cause abstinent smokers to lapse. TimeStitch extracts common sequences of events performed by abstinent smokers from large amounts of mobile health sensor data, and offers a suite of interactive and visualization techniques to enable cohort discovery, construction, and comparison, using extracted sequences as interactive elements. We are extending TimeStitch to support more complex health conditions with high mortality risk, such as reducing hospital readmission in congestive heart failure.

We introduce a novel tool for visualizing netflow traffic on enterprise networks called 3D Parallel Coordinate Planes (3DPCP). This tool provides operators with the ability to manipulate the visual flow of network traffic information by arranging two-dimensional planes along a vertical time axis in 3D space.

A mirror needle holder has been designed to facilitate the passage of a needle through the ciliary sulcus during transscleral fixation of posterior chamber (PC) intraocular lens (IOLs). Two human postmortem eyes were used to demonstrate the efficacy of this mirror used as the needle holder. This method was compared with the current method of passing sutures without view of the ciliary sulcus. The mirror needle holder demonstrated precise passage of the needle into the ciliary sulcus by direct visualization of the ciliary processes and sulcus. This method may avoid the complications associated with transsclerally sutured PC IOLs during penetrating keratoplasty and secondary IOL placement. [Ophthalmic Surg Lasers 1996;27:720-724.]