Data analysis and machine learning for heterogeneous, cross-species data (X02)

• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 460333672

X02 will use the imaging data and mechanical measurements collected within EBM to develop novel machine learning approaches to transfer knowledge across different species and experimental setups. Here, in silico and in vitro analyses will be able to generate more specific, annotated data than in vivo experiments, in particular for human tissues. We will design transfer learning algorithms for heterogeneous data to utilize those data-rich domains and enable the use of machine learning in settings where data and ground truths for supervised learning are difficult to obtain. Thereby, we aim for a combined understanding and representation of imaging and mechanical data across species.

• DFG Programme: Collaborative Research Centres
• Subproject of: SFB 1540: Exploring Brain Mechanics (EBM): Understanding, engineering and exploiting mechanical properties and signals in central nervous system development, physiology and pathology
• Applicant Institution: Friedrich-Alexander-Universität Erlangen-Nürnberg
• Project Heads: Professorin Katharina Breininger; Professor Dr.-Ing. Andreas Maier