Remco Chang

Human–artificial intelligence (AI) interaction (HAI) has emerged as a transformative paradigm in visualization and visual analytics, leveraging both strengths of human expertise, creativity, and iterative refinement. This special issue examines the collaborative potential of HAI, emphasizing how synergistic human–AI partnerships can redefine analytical workflows, enhance trust, and address limitations of fully automated methods. Featuring six accepted articles, this special issue advances our understanding of HAI in visualization through novel theoretical frameworks, empirical studies, and innovative system designs. Collectively, the contributions bridge human ingenuity with AI capabilities and chart directions for future research.

Dashboards serve as effective visualization tools for conveying complex information. However, there exists a knowledge gap regarding how dashboard designs impact user engagement, necessitating designers to rely on their design expertise. Saliency has been used to comprehend viewing behaviors and assess visualizations, yet existing saliency models are primarily designed for single-view visualizations. To address this, we conduct an eye-tracking study to quantify participants' viewing patterns on dashboards. We collect eye-movement data from 60 participants, each viewing 36 dashboards (16 representative dashboards shared across all and 20 unique to each participant), totaling 1,216 dashboards and 2,160 eye-movement data instances. Analysis of the data from 16 dashboards viewed by all participants provides insights into how dashboard objects and layout designs influence viewing behaviors. Our analysis confirms known viewing patterns and reveals new patterns related to dashboard layout designs. Using the eye-movement data and identified patterns, we develop a saliency model to predict viewing behaviors with dashboards. Compared to state-of-the-art models for single-view visualizations, our model demonstrates overall improvement in prediction performance for dashboards. Finally, we propose potential dashboard design guidelines, illustrate an application case, and discuss general scanning strategies along with limitations and future work.

Functionalization is poised to play a prominent role in MOF development as it could become the to-go strategy to bestow extant MOF with new properties, and to control MOF pore shape and size by modulating polymorph selection. Thus, to speed up MOF development through computational work, a better (predictive) understanding on how functionalization impacts MOF synthesizability is needed. Here we use a data-driven approach where molecular dynamics simulations on 5,000+ MOFs are used to shed light on how functionalization affects MOF free energy, as the latter has been largely tied to MOF synthesizability and polymorph selection. More consistently in MOFs with higher void fractions, we find that functionalization generally reduces free energy, with entropy contributing significantly to this thermodynamic stabilization. Although with some functionalizations (-CF3, -F, -Br, -SH, -OH) the role of entropy is more apparent than with others (-CN, -CH3, -NO2, -NH3). Through uneven stabilization of polymorphs, we also find functionalization (more often with -Br, -CN and -CF3) as capable of altering polymorph (topology) selection relative to original non-functionalized polymorphic families. However, no switch in polymorph stability ever occurred when the original (unfunctionalized) polymorphs were separated by more than 1.42 kJ/mol per MOF atom. We show that machine learning can predict functionalization-induced free energy change of a parent MOF with a mean absolute error of 0.16 kJ/mol per atom, using only physical properties of the parent MOF and the functional group as input. The ML-based SHAP analysis agrees with human analysis on the functionalization molecular mass and the hydrogen fraction of the parent MOF being among the factors that influence change in free energy the most. Finally, we present a publicly accessible dynamic interface to visualize and navigate the free energy data , thereby encouraging the research community to engage with and utilize the data to help uncover new insights.

Dimensionality reduction techniques are widely used for visualizing high-dimensional data. However, support for interpreting patterns of dimension reduction results in the context of the original data space is often insufficient. Consequently, users may struggle to extract insights from the projections. In this paper, we introduce DimBridge, a visual analytics tool that allows users to interact with visual patterns in a projection and retrieve corresponding data patterns. DimBridge supports several interactions, allowing users to perform various analyses, from contrasting multiple clusters to explaining complex latent structures. Leveraging first-order predicate logic, DimBridge identifies subspaces in the original dimensions relevant to a queried pattern and provides an interface for users to visualize and interact with them. We demonstrate how DimBridge can help users overcome the challenges associated with interpreting visual patterns in projections.

Visual validation of regression models in scatterplots is a common practice for assessing model quality, yet its efficacy remains unquantified. We conducted two empirical experiments to investigate individuals' ability to visually validate linear regression models (linear trends) and to examine the impact of common visualization designs on validation quality. The first experiment showed that the level of accuracy for visual estimation of slope (i.e., fitting a line to data) is higher than for visual validation of s lope (i.e., accepting a shown line). Notably, we found bias toward slopes that are “too steep” in both cases. This lead to novel insights that participants naturally assessed regression with orthogonal distances between the points and the line (i.e., ODR regression) rather than the common vertical distances (OLS regression). In the second experiment, we investigated whether incorporating common designs for regression visualization (error lines, bounding boxes, and confidence intervals) would improve visual validation. Even though error lines reduced validation bias, results failed to show the desired improvements in accuracy for any design. Overall, our findings suggest caution in using visual model validation for linear trends in scatterplots.

This study presents insights from interviews with nineteen Knowledge Graph (KG) practitioners who work in both enterprise and academic settings on a wide variety of use cases. Through this study, we identify critical challenges experienced by KG practitioners when creating, exploring, and analyzing KGs that could be alleviated through visualization design. Our findings reveal three major personas among KG practitioners - KG Builders, Analysts, and Consumers - each of whom have their own distinct expertise and needs. We discover that KG Builders would benefit from schema enforcers, while KG Analysts need customizable query builders that provide interim query results. For KG Consumers, we identify a lack of efficacy for node-link diagrams, and the need for tailored domain-specific visualizations to promote KG adoption and comprehension. Lastly, we find that implementing KGs effectively in practice requires both technical and social solutions that are not addressed with current tools, technologies, and collaborative workflows. From the analysis of our interviews, we distill several visualization research directions to improve KG usability, including knowledge cards that balance digestibility and discoverability, timeline views to track temporal changes, interfaces that support organic discovery, and semantic explanations for AI and machine learning predictions.