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Conference Topics

Conference Topics 
The primary focus of the conference is on new and original research results in the areas of theoretical findings, design, implementation, and applications. Both theoretical paper and simulation (experimental) results are welcome. Topics of interest include, but are not limited to, the following:


Track 1: Data Engineering and Data Science

 

  • Big Data Management and Analytics

  • Data Architecture and Data Infrastructure

  • Data Integration and Interoperability

  • Cloud-Native Data Systems

  • Data Quality, Governance, and Stewardship

  • Data Mining and Knowledge Discovery

  • Stream Processing and Real-Time Analytics

  • Intelligent Data Platforms and DataOps

  • Scientific Data Management

  • Data Lakes, Data Warehouses, and Data Mesh

  • Data Fabric and Active Metadata

  • Data Observability and Data Lineage

  • Data Virtualization and Data Contracts

  • Synthetic Data Generation and Data Augmentation


Track 2: Intelligent Computing and Emerging Technologies

  • Distributed and Parallel Computing

  • Edge Intelligence and Edge AI

  • Federated Learning and Distributed Intelligence

  • High-Performance Intelligent Computing

  • Cloud-Edge-End Collaborative Computing

  • Neuromorphic Computing and Spiking Neural Networks

  • In-Memory and Near-Memory Computing

  • Quantum AI and Quantum Machine Learning

  • Reconfigurable Computing and Adaptive Architectures

  • Evolutionary Computation

  • Swarm Intelligence

  • Neuro-Symbolic AI

  • Causal Learning and Causal Inference

  • Granular Computing and Rough Sets

  • Approximate Computing and Precision-Scalable Computing


Track 3: AI Theory and Key Technologies

  • Machine Learning and Deep Learning (Supervised, Semi-supervised, Self-supervised)

  • Generative AI (Diffusion Models, GANs, VAEs)

  • Large Language Models (LLMs) and Chain-of-Thought

  • Foundation Models and Multimodal AI (Vision-Language, Audio-Visual)

  • Reinforcement Learning and Autonomous Agents

  • Knowledge Representation and Reasoning (Knowledge Graphs, Symbolic Reasoning)

  • Retrieval-Augmented Generation (RAG) and Knowledge Enhancement

  • Multi-Agent Systems and Collaborative Decision-Making

  • MLOps and LLMOps (Model Operations and Governance)

  • Feature Stores and Model Registries

  • Computer Vision and Image Analysis (recognition, understanding, classification, reconstruction, generation, etc.)


Track 4: Trustworthy AI and AI Security

  • Explainable and Interpretable AI

  • Human-Centric and Responsible AI

  • Cognitive Computing and Intelligent Decision Systems

  • Trustworthy and Secure AI

  • AI Safety and Reliability

  • Adversarial Machine Learning

  • AI Security and Privacy Preservation

  • Differential Privacy and Secure Computation

  • Ethical and Responsible AI Governance

  • Regulatory and Policy Frameworks for AI

  • AI Auditing and Compliance Verification

  • Formal Verification and Certification of AI Systems



Track 5: AI/Data-Driven Innovative Applications of Intelligent Systems

  • Recommender Systems and Personalized Ranking

  • Intelligent Dialogue Systems and Virtual Assistants

  • Autonomous Driving: Perception, Planning, and Control

  • Intelligent Robotics (Manipulation, Navigation, HRI)

  • Intelligent Surveillance and Anomaly Detection

  • AI-Assisted Diagnosis and Medical Image Analysis

  • Intelligent Decision Support Systems (Finance, Operations, Scheduling)

  • Smart Manufacturing and Industry 5.0 (Predictive Maintenance, Quality Inspection)

  • Smart Cities and Urban Computing (Traffic, Energy, Public Safety)

  • FinTech and Intelligent Risk Control

  • Smart Education and Learning Analytics

  • Digital Twin Systems and Simulation Optimization

  • Multimodal Interaction Systems (Speech, Vision, Haptics Fusion)

  • Intelligent Systems for Emergency Management and Disaster Response