• Machine learning theory and foundations
• Deep learning architectures, transformers and optimization
• Representation learning and self supervised learning
• Large language models (LLMs) and foundation models
• Multimodal foundation models (vision language audio)
• Generative AI: diffusion, transformers and multimodal generation
• Retrieval augmented generation (RAG) and knowledge grounded ML
• Learning in knowledge intensive systems
• Reinforcement learning, decision making and sequential agents
• Graph machine learning and network representation learning
• Hybrid ML systems and neuro symbolic AI
• Automated machine learning (AutoML) and hyperparameter optimization
• Explainable AI (XAI), interpretability and model transparency
• AI safety, alignment and responsible AI
• Frontier AI risk, evaluation and governance
• Fairness, accountability and trustworthy ML
• Adversarial ML, robustness, poisoning and model extraction
• Federated learning, privacy preserving ML and secure aggregation
• ML for edge, mobile and resource constrained devices
• Distributed ML, parallel training and systems for LLMs
• ML systems, MLOps and scalable training infrastructure
• Machine learning applications across domains
• ML for healthcare, genomics and medical imaging
• ML for scientific discovery, simulation and surrogate modeling
• ML for climate science, sustainability and environmental modeling
• ML for cybersecurity, threat intelligence and automated defense
• ML for robotics, embodied AI and interactive agents
• Recommender systems and personalization
• Natural language processing and instruction tuned models
• Computer vision, multimodal learning and perception
• Speech processing, audio intelligence and spoken language models
• Time series forecasting, sequential modeling and anomaly detection
• Social network analysis, graph mining and influence modeling
• Data science theory, pipelines and workflow automation
• Data centric AI, data quality optimization and dataset distillation
• Big data analytics and large scale data processing
• Business analytics, decision intelligence and applied DS
• Data engineering, data management and data quality
• Databases, query optimization and modern data systems
• Knowledge discovery, pattern mining and advanced data mining
• Causal inference, causal ML and counterfactual reasoning
• Synthetic data, simulation based generation and digital twins
• ML for finance, economics and market modeling
• Ethical, societal and policy implications of ML/DS

Hard copy of the proceedings will be distributed during the Conference. The softcopy will be available on AIRCC digital library.