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Visualization design often proceeds under unresolved conditions---goals shift, data remain provisional, stakeholder needs evolve, and several plausible directions may remain available at once. Existing visualization frameworks help organize design work and articulate major decisions, yet offer limited explanation of how practitioners proceed before a path forward has become clear. Drawing on an episode-level analysis of a previously collected three-phase qualitative corpus involving eleven expert visualization practitioners, we examine situations in which the problem, representational target, or viable direction remained unsettled. We find that practitioners make such situations actionable through provisional local moves. These moves reveal patterns, distinctions, and interpretive possibilities; clarify what is tractable, viable, or worth pursuing; and sometimes reorient the work itself. The analysis shows that situated action, professional judgment, and explicit design reasoning are intertwined in expert practice. It also identifies a practical limit on how fully design activity can be specified in advance. When the meaning of the next move depends on what a situation reveals in response to action, prescriptive decision structures cannot fully determine the course of design. The paper contributes an empirical account of situatedness in visualization practice and explains how local action makes unresolved work interpretable enough for consequential design decisions.

Interdisciplinary research collaboration is crucial for scientific innovation, but it remains difficult to initiate in practice. Existing collaborator discovery approaches are often constrained by disciplinary boundaries and static researcher profiles that do not reflect the specific context of a new collaboration goal. As a result, researchers struggle to translate open-ended collaboration goals into domain-specific tasks, evaluate candidate researchers' fit and complementarity, and establish common ground before initial contact. To address these challenges, we present DeepConnect, an LLM-augmented visual analytics system for interdisciplinary collaborator discovery. DeepConnect translates collaboration ideas into domain-specific tasks, retrieves relevant papers to ground cross-domain exploration, and provides coordinated visualizations for exploring and comparing candidate researchers. It further reveals terminology gaps and overlaps across domains and supports publication-grounded conversation rehearsal to help users prepare for outreach. We evaluate DeepConnect through two case studies, a user study, and a component-level evaluation, showing its value for complementary team formation, idea refinement, and pre-contact preparation. The DeepConnect website is available at https://deepconnect.sg.

Remote and hybrid data-analysis meetings force collaborators to split attention between shared visualizations and separate video galleries, weakening deictic reference, nonverbal grounding, and shared awareness. We present ManyHans, a web-based collaborative visualization system that embeds participants’ video streams directly into the visualization space and supports hybrid interaction through mouse input and mid-air hand gestures. ManyHans contributes three design ideas for collaborative visual analysis: (1) embodied pointing and annotation that let participants reference data with their hands inside the shared visual workspace, (2) on-demand video embedding that brings a participant into the visualization when presenting while preserving a conventional gallery view for exploration, and (3) a snapping fisheye lens that combines semantic zoom with Voronoi-based target expansion to stabilize gesture-based selection in dense network visualizations. We developed the system through a RITE-style iterative design process with 26 participants across nine group sessions, then validated the final design in three additional group sessions. Our findings show that embedded video and gesture-based reference improve engagement, grounding, and communication around data, but prolonged mid-air interaction introduces fatigue and reduced precision during exploration. From these results, we distill a central design implication for collaborative visualization: embodied interaction should be available as a lightweight presentation and handoff mechanism within a hybrid interface, rather than as the primary mode for sustained analysis.

Research on collaborative analytics has long recognized that data representations enable collaborative sensemaking by establishing a common point of reference. Yet research on visualization accessibility has largely approached data representations in terms of individual comprehension—whether they enable a blind reader to independently extract equivalent information. In this paper, we draw on linguistic anthropology to understand data representations' central role in mediating communication in collaborative, mixed-ability settings that include both blind and sighted data analysts. Through a contextual inquiry with Bower Lab, an oceanography lab led by blind PI Amy Bower, we show how multi-modal representations provide shared linguistic affordances for data reference and social signaling that enable collaborators to establish common ground and manage joint attention. These findings suggest a reframing of accessibility in visualization as effective bidirectional communication—for instance, tactile representation not only makes data accessible non-visually but also makes a blind analyst's analysis process accessible to sighted collaborators.

Figurative art, as a culturally embedded medium, encodes narrative, symbolic, and emotional meanings that reflect artistic choices and historical realities. The recent availability of large-scale digital collections of figurative artworks creates opportunities for computational analysis, but existing methods mostly focus on classification or style detection, lacking structured modeling of high-level figurative elements and integration of cultural context. We present DAVA, a visual analytics system that supports interdisciplinary exploration of figurative art. First, we model paintings across three structural levels: facial expressions (micro), posture features (meso), and object co-occurrence (macro). Second, we employ a vision–language model to discover latent patterns from these features and present them through novel visualization designs. Third, we introduce domain-informed AI agents that simulate interdisciplinary research teams to interpret artworks in cultural and historical context. To evaluate DAVA, we first conducted a quantitative evaluation demonstrating the accuracy and consistency of the multi-agent interpretation mechanism. Case studies and expert interviews then confirmed the system’s utility and support for semantically and historically informed exploration of figurative art.

Visual analytics has the potential to provide critical support in computer architecture design by helping architects interpret execution behavior, but current tools are limited to exposing what happened during execution rather than why, relying on user expertise rather than visual evidence to identify true causes of performance issues. Expertise can help analysts formulate hypotheses, but analysts cannot validate those hypotheses through visualization when causal factors are not visible. We design a visual analytics system that makes causal factors, such as task blocking and underlying mechanisms, visible. At the core of our approach is a conceptual abstraction, the Milestone, a data primitive that simultaneously marks the resolution of prior blocking conditions and assigns reasons to waiting states. Milestones convert execution traces from records of activity into records of causation. We further design three complementary visualizations that leverage Milestones, enabling us to show summaries of blocking patterns, analyze how patterns evolve over time, and navigate causal relationships across parent-subtask hierarchies. We evaluate our approach through a study with GPU architecture researchers, showing that the abstraction and resulting visualizations enable participants to validate hypotheses about an architecture's performance directly from visual evidence. Finally, we discuss how the Milestone abstraction can be adapted to support causal analysis in other expertise-intensive domains.

DVD: Scaffolding Non-experts' Ideation of Data Videos through Co-creation with AI