The fund investment industry heavily relies on the expertise of fund managers, who bear the responsibility of managing portfolios on behalf of clients. With their investment knowledge and professional skills, fund managers gain a competitive advantage over the average investor in the market. Consequently, investors prefer entrusting their investments to fund managers rather than directly investing in funds. For these investors, the primary concern is selecting a suitable fund manager. While previous studies have employed quantitative or qualitative methods to analyze various aspects of fund managers, such as performance metrics, personal characteristics, and performance persistence, they often face challenges when dealing with a large candidate space. Moreover, distinguishing whether a fund manager's performance stems from skill or luck poses a challenge, making it difficult to align with investors' preferences in the selection process. To address these challenges, this study characterizes the requirements of investors in selecting suitable fund managers and proposes an interactive visual analytics system called FMLens. This system streamlines the fund manager selection process, allowing investors to efficiently assess and deconstruct fund managers' investment styles and abilities across multiple dimensions. Additionally, the system empowers investors to scrutinize and compare fund managers' performances. The effectiveness of the approach is demonstrated through two case studies and a qualitative user study. Feedback from domain experts indicates that the system excels in analyzing fund managers from diverse perspectives, enhancing the efficiency of fund manager evaluation and selection.

The process of labeling medical text plays a crucial role in medical research. Nonetheless, creating accurately labeled medical texts of high quality is often a time-consuming task that requires specialized domain knowledge. Traditional methods for generating labeled data typically rely on rigid rule-based approaches, which may not adapt well to new tasks. While recent machine learning (ML) methodologies have mitigated the manual labeling efforts, configuring models to align with specific research requirements can be challenging for labelers without technical expertise. Moreover, automated labeling techniques, such as transfer learning, face difficulties in in directly incorporating expert input, whereas semi-automated methods, like data programming, allow knowledge integration through rules or knowledge bases but may lack continuous result refinement throughout the entire labeling process. In this study, we present a collaborative human-ML teaming workflow that seamlessly integrates visual cluster analysis and active learning to assist domain experts in labeling medical text with high efficiency. Additionally, we introduce an innovative neural network model called the embedding network, which incorporates expert insights to generate task-specific embeddings for medical texts. We integrate the workflow and embedding network into a visual analytics tool named KMTLabeler, equipped with coordinated multi-level views and interactions. Two illustrative case studies, along with a controlled user study, provide substantial evidence of the effectiveness of KMTLabeler in creating an efficient labeling environment for medical text classification.

The burgeoning online video game industry has sparked intense competition among providers to both expand their user base and retain existing players, particularly within social interaction genres. To anticipate player churn, there is an increasing reliance on machine learning (ML) models that focus on social interaction dynamics. However, the prevalent opacity of most ML algorithms poses a significant hurdle to their acceptance among domain experts, who often view them as "opaque models". Despite the availability of eXplainable Artificial Intelligence (XAI) techniques capable of elucidating model decisions, their adoption in the gaming industry remains limited. This is primarily because non-technical domain experts, such as product managers and game designers, encounter substantial challenges in deciphering the "explicit" and "implicit" features embedded within computational models. This study proposes a reliable, interpretable, and actionable solution for predicting player churn by restructuring model inputs into explicit and implicit features. It explores how establishing a connection between explicit and implicit features can assist experts in understanding the underlying implicit features. Moreover, it emphasizes the necessity for XAI techniques that not only offer implementable interventions but also pinpoint the most crucial features for those interventions. Two case studies, including expert feedback and a within-subject user study, demonstrate the efficacy of our approach.

In healthcare, AI techniques are widely used for tasks like risk assessment and anomaly detection. Despite AI’s potential as a valuable assistant, its role in complex medical data analysis often over-simplifies human-AI collaboration dynamics. To address this, we collaborated with a local hospital, engaging six physicians and one data scientist in a formative study. From this collaboration, we propose a framework integrating two-phase interactive visualization systems: one for Human-Led, AI-Assisted Retrospective Analysis and another for AI-Mediated, Human-Reviewed Iterative Modeling. This framework aims to enhance understanding and discussion around effective human-AI collaboration in healthcare.

The provision of fire services plays a vital role in ensuring the safety of residents' lives and property. The spatial layout of fire stations is closely linked to the efficiency of fire rescue operations. Traditional approaches have primarily relied on mathematical planning models to generate appropriate layouts by summarizing relevant evaluation criteria. However, this optimization process presents significant challenges due to the extensive decision space, inherent conflicts among criteria, and decision-makers' preferences. To address these challenges, we propose FSLens, an interactive visual analytics system that enables in-depth evaluation and rational optimization of fire station layout. Our approach integrates fire records and correlation features to reveal fire occurrence patterns and influencing factors using spatiotemporal sequence forecasting. We design an interactive visualization method to explore areas within the city that are potentially under-resourced for fire service based on the fire distribution and existing fire station layout. Moreover, we develop a collaborative human-computer multi-criteria decision model that generates multiple candidate solutions for optimizing firefighting resources within these areas. We simulate and compare the impact of different solutions on the original layout through well-designed visualizations, providing decision-makers with the most satisfactory solution. We demonstrate the effectiveness of our approach through one case study with real-world datasets. The feedback from domain experts indicates that our system helps them to better identify and improve potential gaps in the current fire station layout.

Simulation-based Medical Education (SBME) has been developed as a cost-effective means of enhancing the diagnostic skills of novice physicians and interns, thereby mitigating the need for resource-intensive mentor-apprentice training. However, feedback provided in most SBME is often directed towards improving the operational proficiency of learners, rather than providing summative medical diagnoses that result from experience and time. Additionally, the multimodal nature of medical data during diagnosis poses significant challenges for interns and novice physicians, including the tendency to overlook or over-rely on data from certain modalities, and difficulties in comprehending potential associations between modalities. To address these challenges, we present DiagnosisAssistant, a visual analytics system that leverages historical medical records as a proxy for multimodal modeling and visualization to enhance the learning experience of interns and novice physicians. The system employs elaborately designed visualizations to explore different modality data, offer diagnostic interpretive hints based on the constructed model, and enable comparative analyses of specific patients. Our approach is validated through two case studies and expert interviews, demonstrating its effectiveness in enhancing medical training.