Scope

For the Research track, we invite submission of papers describing innovative research on all aspects of knowledge discovery, data science and AI, ranging from theoretical foundations to novel models and algorithms for applied problems in science, business, medicine, and engineering. Visionary papers on new and emerging topics are also welcome, as are application-oriented papers that make innovative technical contributions to research.  Topics of interest include, but are not limited to:

    Foundations of Knowledge Discovery and Data Science. Submissions are invited to discuss core models, algorithms, and theoretical insights for knowledge discovery. Topics may include data-driven learning and structured knowledge extraction, including supervised, unsupervised, semi-supervised, and self-supervised learning, classification, regression, clustering, and dimensionality reduction; model selection and optimization; probabilistic and statistical methods (e.g., Bayesian inference, graphical models); matrix and tensor methods; structured and relational learning from data.

    Modern AI and Big Data. Submissions are invited to elaborate on the intersection of AI and massive data repositories. Topics may include deep representation learning, meta-learning, in-context learning, prompt engineering, continual learning, few-shot adaptation, reinforcement learning, generation, and reasoning, including generative models (e.g., GANs, VAEs), large language models (LLMs), and multimodal foundation and frontier models operating on big data; AI’s role in emergent reasoning, automated insight generation, and scientific discovery, including knowledge graph construction, hypothesis generation, neural-symbolic integration, and deriving novel concepts from large complex data.

    Trustworthy and Responsible Data Science. Submissions are invited to feature techniques and frameworks that ensure responsible data use, management, and analysis. Topics may include data security, data privacy, data transparency, accountability in data-driven systems, privacy-preserving learning, adversarial robustness, interpretability and explainability of models, decision support visualization, fairness in data mining, ethical data processing, algorithmic auditing, and frameworks for responsible AI development and deployment.

    Systems for Data Science and Scalable AI. Submissions that detail new architectures, systems, and infrastructures for large-scale data analysis and machine learning (e.g., distributed computing, federated learning, cloud-based systems) are invited. Topics may include efficient approaches to support high-volume data analysis, streaming, sampling, and summarization, data integration, transformation, and cleaning at scale, and data mining and machine learning for systems—machine learning for database management, learning device placement, orchestration, and scheduling of computational and data workflows.

    Data Science Applications. Submissions are invited for innovative data science and artificial intelligence (AI) applications. Topics may include methods for analyzing scientific, social science, medical, and legal data, as well as time series, text, graphs, Internet of Things (IoT) data, and more. We also welcome contributions on recommender systems and bioinformatics. New directions that push the boundaries of data science applications are of particular interest, such as quantum data science, including algorithms and information-theoretic approaches for quantum machine learning and data processing in quantum systems.

Survey papers that seek to provide a comprehensive understanding of the current state of research on a specific topic, rather than to contribute a novel intellectual contribution, is out of scope.