We encourage submissions of high-quality research papers on the general areas of artificial intelligence, data science, databases, information retrieval, and knowledge management. Topics of interest include:

• Data Acquisition and Processing: IoT data, data quality, data privacy, mitigating biases, data wrangling, data exploration, data preparation, valuation, and tradin
• Data Integration and Aggregation: semantic processing, data provenance, data linkage, data fusion, knowledge graphs, data warehousing, data lakes, privacy and security, modeling, information credibility, AI-generated content detection and provenance)
• Efficient Data Processing: serverless computing, data-intensive computing, database systems, indexing and compression, architectures, distributed data systems, dataspaces, customized hardware
• Special Data Processing: multilingual text, sequential, stream, time series, spatio-temporal, (knowledge) graph, multimedia, scientific, and social media data
• Analytics and Machine Learning: OLAP, data mining, machine learning and AI, scalable analysis algorithms, algorithmic biases, event detection and tracking, interpretability and explainability
• Foundation Models and Neural Information Processing: large language models, graph neural networks, domain adaptation, transfer learning, in-context learning, fine-tuning and alignment, network architectures, neural ranking, neural recommendation, and neural prediction
• Agentic AI for Information and Knowledge Tasks: tool use, planning, multi-agent systems, autonomous retrieval and decision-making, agentic workflows, orchestration of knowledge-intensive processes
• Information Access and Retrieval: retrieval-augmented generation (RAG), retrieval models, query processing, question answering and dialogue systems, open-ended question answering, conversational information seeking, generation of knowledge graphs from unstructured data, personalization, recommender systems, filtering systems
• Trustworthy and Responsible AI: fairness, accountability, ethics, explainability, safety, alignment, robustness, hallucination detection and mitigation, factuality and grounding, attribution, responsible deployment
• Users and Interfaces for Information Systems: user behavior analysis, user interface design, perception of biases, interactive information retrieval, interactive analysis, spoken interfaces, human-AI collaboration, co-pilot paradigms, human-in-the-loop systems
• Evaluation: performance studies, benchmarks, online and offline evaluation, best practices, evaluation of generative and LLM-based systems, human evaluation protocols, LLM-as-judge, reproducibility
• Crowdsourcing: task assignment, worker reliability, optimization, trustworthiness, transparency, crowdsourcing in the era of large language models
• Mining Multi-Modal Content: natural language processing, speech recognition, computer vision, content understanding, knowledge extraction, knowledge representations, multi-modal foundation models
• Data Presentation: visualization, summarization, readability, VR/AR, speech input/output
• Generative AI for Data and Knowledge Management: GenAI for structured and unstructured data processing, GenAI for data synthesis and simulation, GenAI for information summarization, content creation, and visualization, synthetic data generation, and quality
• Resource-Efficient AI: model compression, quantization, distillation, distributed learning, inference optimization, on-device models, leveraging edge computing to reduce computational overhead

Applications: urban systems, biomedical and health informatics, legal informatics, crisis informatics, computational social science, data-enabled discovery, social networks, education, business