The research activity covers a large spectrum of research in data science, artificial intelligence and information systems, including biomedical signal and image processing, computer vision, processing and analysis for high dimensional and complex data, as well as machine learning and inference theory and algorithms. IEM is among the world leading institutes in signal processing and machine learning, and has a long tradition of excellence with strong connections to other EPFL schools, and European as well as world-wide collaboration networks. IEM researchers have developed unique strengths on both the fundamental aspects of information processing and inference, methods and algorithms, as well as groundbreaking contributions in different applications domains such as robotics and healthcare for example.

Key research themes

• Machine learning: data analysis, classification, deep learning, interpretable algorithms, robust models, optimization, graph and networks.
• Learning and inference: learning and inference systems, distributed algorithms, adaptive systems, data-driven engineering systems, complex and networked systems.
• Signal and image processing: high dimensional data processing, sparsity and low-dimensional models, inverse problems, fast algorithms.
• Sensing and acquisition: information processing for data science, denoising, sensing models, multisensory systems.
• Application domains: medical imaging, image analysis, speech processing, computer vision, secure and immersive communication, robotics, intelligent systems, scientific machine learning.