We seek contributions in, but not limited to, the following areas:

Foundational Techniques in Machine Learning and AI
Supervised, unsupervised, semi-supervised and self-supervised learning.
Deep learning and representation learning.
Reinforcement learning and federated learning.
Transfer learning, meta learning, few-shot and continual learning.
Multitask and multimodal learning.
Generative models, including GANs and diffusion models.
Large Language Models (LLMs) and Large Multimodal Models (LMMs).
Zero-shot and prompt-based learning.
Learning from Diverse and Complex Data
Analytics over structured, semi-structured, and unstructured data.
Text, time-series, graph, spatial, spatio-temporal, and network data.
Web, social media, multimedia, IoT, and sensor data.
Sequential, temporal, and dynamic data modelling.
Data-Centric AI and Data Engineering
Data preprocessing, cleaning, integration, matching, and linkage.
Privacy-preserving and secure data mining.
Data-centric AI pipelines and dataset curation.
Computational aspects of data mining and large-scale data management.
Scalable and Real-Time Data Analytics
Big data analytics and scalable ML.
Parallel and distributed learning algorithms.
Data stream mining and real-time analytics.
Edge, cloud, and IoT-enabled ML systems.
Interactive and Visual Analytics
Visual analytics and explainability through visualisation.
Human-in-the-loop machine learning.
Interactive data exploration and decision support.
Responsible, Causal, and Explainable AI
Explainable and interpretable machine learning.
Fairness, accountability, transparency, and ethics in AI.
Causal inference and causal machine learning.
Robustness, generalization, and uncertainty quantification.
Applied Data Science and ML Across Domains
Applications in business, finance, education, agriculture, urban planning, healthcare, sports, social sciences, cybersecurity, arts, and humanities.
Domain-specific AI systems in biomedical informatics, environmental science, astronomy, engineering, and more.
Industrial case studies and data-driven product innovations.