IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2026

The IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2026 covers cutting-edge research and applications in machine learning for signal processing, focusing on advanced techniques and real-world implementations.

Key Topics

• Distributed and federated learning
• Machine learning on graphs
• Tensor-based signal processing
• Applications of machine learning
• Deep learning techniques
• Privacy and fairness in machine learning
• Virtual and augmented reality data processing
• Meta and transfer learning
• Learning theory and algorithms
• Graph representation learning
• Matrix and tensor learning methods
• Kernel and dictionary learning methods
• Subspace and manifold learning
• Advanced optimization methods
• Information theory for learning
• Bayesian and probabilistic inference
• Domain-aware processing
• Learning from multimodal data
• Machine intelligence for education
• Biosignal processing and learning
• Decentralised and edge communication, computing, and processing
• Large Language Models
• Multimodal Machine Learning
• MLSP in Financial Engineering (Fintech)
• MLSP in Healthcare and Aged Care
• MLSP in Disaster Management
• MLSP in Wireless Communications
• MLSP in Neuroscience Applications

Who should Attend

• Researchers in machine learning and signal processing
• Data scientists and engineers
• Academics and students in related fields
• Professionals in healthcare and financial technology
• Experts in disaster management and wireless communications
• Engineers working with biosignal processing
• Developers of virtual and augmented reality systems