Theory and Algorithmics
Using mathematics and algorithms to unlock the full potential of biological imaging.
Modern biological imaging is increasingly limited not by the microscopes themselves, but by how experiments are designed, how imaging data are acquired, and how reliably information can be recovered from imperfect measurements. The Theory and Algorithmics Platform develop mathematical models and computational methods that improve every stage of the imaging pipeline, enabling researchers to acquire better data, reconstruct higher-quality images, and extract more meaningful biological insight.
What?
We work with researchers to improve the design, performance, and analysis of imaging experiments. Our expertise spans the entire imaging pipeline, including:
- Mathematical modelling of imaging systems, biological processes, and experimental constraints.
- Experimental design, developing acquisition strategies that maximise image quality while minimising dose, distortion, and other physical limitations.
- Image reconstruction and analysis, creating computational methods that are robust, efficient, and reliable, including machine learning approaches.
- Theoretical understanding of the physical limits of imaging technologies, enabling better interpretation of experimental data and improved imaging performance.
- Collaborative algorithm development, working alongside experimental scientists to translate mathematical ideas into practical imaging workflows.
Why?
Every imaging experiment is constrained by physics. Noise, limited dose, distortion, incomplete measurements, and acquisition time all determine how much biological information can ultimately be recovered. Improving microscope hardware alone cannot overcome these limitations. Our role is to ensure that experiments are designed and analysed in the most information-efficient way possible, enabling researchers to obtain higher-quality data, more reliable reconstructions, and deeper biological insight.
How?
Our approach combines mathematics, physics, computer science, and experimental science. We develop theoretical models to understand the limits of imaging systems, use these models to design improved acquisition and reconstruction methods, and validate the resulting algorithms on real experimental data before integrating them into biological imaging workflows.
