In our research, we develop statistical and mathematical methods to solve real-world challenges. We specialize in inverse problems; tomographic and photoacoustic imaging; signal and image processing; and machine learning, with a focus on deep learning. Our work delivers practical impact grounded in rigorous theory, and is conducted in an interdisciplinary, open, and sustainable manner.

We collaborate closely with mathematicians, physicists, engineers, clinicians, and industry partners. We believe strongly that interdisciplinary, cross-sector research accelerates scientific progress and benefits society.

Current focus areas:

• Inverse problems and regularisation theory
• Computed tomography and other tomographic imaging modalities
• Photoacoustic imaging
• Medical image analysis
• Signal and image processing
• Machine learning and data-driven methods (including deep learning)