Tools of the Tradebookmark_border

This paper presents lpviz, a browser-based visualization tool for linear programming. lpviz is deeply interactive, offering an intuitive interface where users can directly draw and edit the feasible region and objective vector, without requiring cumbersome manipulation of raw numerical coefficients. lpviz lets users compare the behavior of several classes of linear programming algorithms, namely Simplex, Interior-Point, Primal-Dual Hybrid Gradient, and Central Path. In the 3D mode, lpviz places iterates at heights corresponding to important solver metadata such as complementarity gap or KKT residual, helping users gain further insight into algorithm behavior beyond the primal iterates alone. lpviz can be used in both research and classroom settings, to help develop intuition for the strengths and weaknesses of different solvers and the impact of solver settings on convergence behavior. lpviz is open-source, permissively licensed, and freely available on any device with a web browser at https://lpviz.net.

Visualization has been recognized as a valuable means of supporting debugging by externalizing runtime behavior that would otherwise remain hidden or scattered. However, most visual debugging research has focused on traditional software development settings, leaving the distinct challenges of data-intensive workflows largely uncharacterized. To build visual debugging support for these settings, we first need to characterize how practitioners debug in these settings and translate their challenges into concrete visualization opportunities. To this end, we conducted semi-structured interviews with nine participants from diverse data-intensive domains and analyzed the data using thematic analysis. Our analysis reveals three cross-cutting challenges: assembling fragmented evidence, detecting expected--observed discrepancies, and tracing state evolution across workflow components. We distill these challenges into three concrete requirements that current debuggers support only partially but that visualization is well suited to address: cross-artifact evidence alignment, expectation-grounded comparison, and traceable state evolution. Together, these requirements begin to characterize a design space for future visual debugging research in data-intensive programming.

Tuning symbolic execution engines requires configuring tens of parameters, and researchers often run multiple tuners on one program, producing Multi-Tuner Parameter Optimization Logs (MTPOL). However, existing tools reduce each tuner run to a scalar metric, obscuring where it searches and how its coverage complements other tuners’. We present TuneScape, a map visualization that lays out MTPOL over a shared hexagonal partition of the parameter space, allowing researchers who run multiple tuners to contrast them, inspect the parameter values that drive their differences, and find complementary regions. Two use cases, on a KLEE benchmark and a hyperparameter optimization log, illustrate how TuneScape shows tuner-specific search regions and the parameter values that characterize them, and helps locate cross-tuner combinations that extend branch coverage beyond any single tuner in these cases.

Extended reality (XR) is increasingly a setting for empirical visualization research, where study-relevant state is distributed across headset and controller pose, scene configuration, selections, spatial layouts, and AR anchors. Existing XR tools support parts of the workflow, such as scene authoring, interaction, or session analysis, yet seldom treat an XR stimulus as a reusable component of a complete study lifecycle. We present reVISit-XR, an extension of reVISit that makes customizable WebXR stimuli embeddable, trackable, and replayable within empirical visualization studies. reVISit-XR sequences XR scenes alongside standard study components, collects reactive task responses, captures scene-authored semantic state together with generic XR traces, and rehydrates participant sessions for later desktop and headset analysis. Organized as a reusable stimulus build package and a study integration package, it lets XR stimuli act as first-class study components. We demonstrate its scope and feasibility through seven integrated, reusable examples and a deployed study, and discuss its current capabilities and future extensions.

Data visualization is an essential component of mobile health (mHealth) apps for delivering users' personal health data. However, creating effective visualizations for mobile and wearable devices is challenging: screen space is limited, touch interaction is coarse, and developers may lack the visualization expertise needed to design for small screens and non-expert users. Existing general-purpose chart libraries compound this difficulty by ignoring health-specific contexts, leaving each developer to make independent design decisions about how health data should be presented. The result is inconsistent and often poorly designed visualizations across apps. We present SalusChart, an open-source Android visualization library built with Jetpack Compose, specifically designed for mHealth apps. Its reusable components encode visualization design principles and support the development of more consistent visualizations across mHealth apps on mobile and wearable devices.

In historical studies, the recording convention of patrilineal genealogies systematically conceals the cross-clan connections formed by women through marriage, resulting in the structural disintegration of ancient kinship networks into isolated family trees. Existing graph neural network link prediction methods can generate candidate marriage pairs, but they cannot provide interpretable reasons; and relying solely on large language model reasoning is difficult to process such a large-scale and highly complex data structure in a single round, leading experts to find it difficult to trust or review recommended results. We introduce GeneaLink, a complementary and reversible interactive visual analysis system that combines Heterogeneous Graph Transformer (HGT) and multi-round Multi-agent Simulation, enabling historians to examine model confidence, trace the source of predictions, inject domain knowledge into the deliberation process of agents, and confirm or revoke decisions under complete audit trails. An expert-guided system walkthrough demonstrates the effectiveness of GeneaLink.

The intersection of AI, healthcare, and visualization is evolving rapidly, posing challenges that cut across disciplinary boundaries and resist easy resolution. The Visual Analytics in Healthcare workshop (VAHC), co-located every other year at the IEEE VIS conference and the AMIA (American Medical Informatics Association) annual conference, has served as a forum to connect the visualization and medical informatics community since 2010. In 2025, to celebrate the 16th edition, we used the workshop as an opportunity to consolidate the community’s collective experience (and expertise) and identify Grand Challenges where the field should prioritize going forward. We combined thematic coding of the 15 accepted VAHC workshop papers with structured group discussions among more than 40 participants, organized around three major themes: “Technical innovation vs. clinical reality”, “Human-centered and scalable VAHC”, and “From foundations to actionable insights”, followed by post-workshop reflexive analysis. Across all three groups, AI emerged as the most consistently recurring concern. In this paper, we report our AI-centered insights from the VAHC 2025 group activity, contextualize them against the broader literature along five Grand Challenges themes, and distill them into five challenge clusters, each concluded with recommendations for future research directions that cross disciplinary boundaries: (1) trust and bias, (2) data and infrastructure, (3) explainability and communication, (4) human-AI interaction, and (5) model reliability and validation. We share these challenges and their associated research directions as a starting point for discussion and collaboration across the healthcare, AI, and visualization communities. All supplemental materials are available at https://osf.io/p79uj.