Do as eye do: efficient content-adaptive processing and storage of large fluorescence images

Dr Bevan Cheeseman is an Applied Mathematician and recent PhD graduate from the Mosaic Group at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany. He is interested in using fluorescence imaging and a data-driven approach to understand how cells during development get to the right place, at the right time, wearing the right hat despite all the distractions. Such a data-driven approach requires dealing with the bottleneck of the massive processing and storage resources needed when using 3D+t images. To address this bottleneck, Bevan has developed a content-adaptive image representation, the Adaptive Particle Representation (APR). The APR uses spatially adaptive sampling focusing storage, memory, and computation resources to the important parts of the data in both space and time. By reducing required resources and processing time by factors of 10-100 the APR can enable high-throughput experimental and processing pipelines, which can form the basis of a data-driven approach. Bevan is also interested in the development of content-adaptive processing algorithms and data-driven model-discovery techniques that allow the discovery of driving dynamics (e.g. partial-differential equations) directly from experimental data.