GLAM: Generative lung architecture modeling
Complex 3D tissue disease models are being used as surrogates for pre-clinical drug development. However, current methods for fabricating artificial tissues are based on image-derived, or manually designed tissue architectures, and lack high-throughput applicability. This project is developing generative methods for designing bio-printable lung tissues across a spectrum of disease severity in the specific context of mouse and human lung disease.
Other projects
Foundations of Supervised Deep Learning for Inverse Problems
Recently, deep learning methods have excelled at various data processing tasks including the solution of ill-posed inverse problems. The goal of this project is to contribute to the theoretical foundation for truly understanding deep networks as regularization techniques which can reestablish a continuous dependence of the solution on the data.
SFB Transregio 154 – C06: Transport metrics for analysis and optimization of network problems
SFB TRR 154 is a project of the German Research Foundation (DFG) and combines integer-continuous methods, model adaptation, and numerical simulation, to analyze and optimize gas markets, infrastructure, and control of networks. The third funding period specifically focuses on the transition from natural gas to hydrogen.