SAIM 2026 invites submissions on all areas of Artificial Intelligence, Machine Learning, and Soft Computing, including but not limited to:

• Foundations of Artificial Intelligence

• Machine Learning Theory, Optimization and Algorithms

• Deep Learning Architectures and Training Techniques

• Reinforcement Learning and Decision Making

• Probabilistic Modeling and Bayesian Methods

• Trustworthy, Explainable and Interpretable AI

• Fairness, Accountability, Transparency and Ethics in AI

• Causality in Machine Learning

• Continual, Lifelong and Transfer Learning

• Multimodal Learning and Foundation Models

• Generative AI (Text, Image, Audio, Video)

• Autonomous Agents and Multi Agent Systems

• Computer Vision and Pattern Recognition

• 3D Vision, Scene Understanding and Reconstruction

• Vision Language Models

• Natural Language Processing and Large Language Models

• Information Retrieval, Search and Web Intelligence

• Knowledge Representation, Reasoning and Knowledge Graphs

• Conversational AI and Dialogue Systems

• Bioinformatics, Computational Biology and Biomedical AI

• AI for Healthcare, Precision Medicine and Medical Imaging

• Neurosymbolic and Cognitive Computing

• AI for Scientific Discovery, Simulation and Modeling

• Data Mining, Big Data Analytics and Scalable ML

• Data Visualization and Exploratory Analysis

• Social Network Analysis and Computational Social Science

• E-commerce, FinTech and Smart Economy Applications

• AI for Climate Science, Sustainability and Smart Cities

• Distributed, Parallel and High Performance AI Systems

• Edge AI, TinyML and Embedded Intelligence

• Neuromorphic and Morphic Computing

• AI Optimized Hardware and Accelerators

• Nano/Micro Systems for Intelligent Computing

• Soft Computing Theory and Applications

• Fuzzy Logic, Fuzzy Systems and Rough Sets

• Evolutionary Computing, Swarm Intelligence and Optimization

• Hybrid Intelligent Systems

• Robotics, Autonomous Systems and Intelligent Control

• Human Robot Interaction and Embodied AI

• Cyber Physical Systems and Intelligent Networks

• AI Security, Privacy and Adversarial Machine Learning

• Robustness, Safety and Reliability of AI Systems

• Quantum Machine Learning

• Benchmarking, Evaluation and Reproducibility in AI

• AI for Creativity, Art, Media and Cultural Analytics