New associate member Daniel Razansky

The research of Daniel Razansky aims to devise novel tools for high performance functional and molecular imaging so as to deliver biological and clinical information presently not attainable with existing state-of-the-art imaging modalities. His group approaches this in several diverse ways, from the establishment of solid theoretical background, imaging instrumentation, inverse and machine learning methods, to the development of in-vivo imaging methodologies and contrast enhancement approaches.

The research group of Daniel Razansky is one of seven at the Institute for Biomedical Engineering (IBT), a joint institution of the ETH Zurich and the University of Zurich. Three further groups at IBT are also involved with the Center for Learning Systems, those of Prof. Klaas Pruessmann, Prof. Klaas Enno Stephan and Prof. Mehmet Fatih Yanik (Affiliated Lab). The groups' research combines the expertise of natural sciences and electrical engineering with biology and medicine, from imaging and sensing through to modeling and the prediction of health and disease. Within the CLS context, this allows for new directions in healthcare, biotechnology and manufacturing, for example with the development of medical microrobots.

For an example of the research of Professor Razansky's group, see this recent publication: Davoudi, N, Deán-Ben, XL & Razansky, D (2019). Deep learning optoacoustic tomography with sparse data. Nature Machine Intelligence volume 1, 453–460. doi.org/10.1038/s42256-019-0095-3.