Microbial communities in water resource recovery facilities are important for environmental protection, resource recovery, and bioenergy production. The challenge today is that many of the factors controlling these communities are poorly understood. In the project “Explainable AI for Complex Microbial Community Interactions and Predictions”, new deep learning models are brought into play to investigate and understand the complex interactions between bacteria.
“By understanding the key factors involved when bacteria assemble in communities, we will be able to predict the dynamics involved and thereby optimize the performance of facilities recycling waste water into clean water,” Chenjuan Guo explains.
In addition to the obvious resource recovery benefits, the project is expected to provide a deeper understanding of data driven methods implemented to support decision making.
“We anticipate that the project explains the inner work and output of data driven methods, especially machine learning models, such that the methods can be better understood and utilized by various stakeholders to facilitate their decision making,” says Chenjuan Guo.
The Villum Synergy grants are awarded in support of datadriven interdisciplinary research projects. This year, 12 projects receive in total DKK 45 million including three projects involving researchers from the Technical Faculty of It and Design, Aalborg University.