The mathematics research landscape at MPI-CBG is advancing our understanding of complex biological systems. We are developing novel theoretical frameworks, sophisticated computational tools, and innovative analytical techniques for gaining insight into ever-growing biological datasets.
Mathematics here is unified by the commitment to move beyond linear approaches to capture the inherent nonlinearities and high dimensionality of biological phenomena. Our expertise spans a powerful spectrum of disciplines: algebra, topology, geometry, dynamical systems, network theory, combinatorics, and--crucially--machine learning and scientific computing. These converge to create a vibrant environment within which we reveal hidden patterns, classify intricate structures, and model dynamic processes across different scales of biological organization.
We share a strong emphasis on sophisticated mathematical and computational formalism. This outlook is a necessary premise from which to reveal a more profound understanding of biological shapes, structures, and their evolution in space and time.
This integrative approach has led to advances combining theory, innovative computational methods, high-resolution imaging, and spatially resolved molecular profiling to quantify patterns in tissues, identify cell types from gene expression data, and build predictive models of molecular cell dynamics.
The mathematics and machine learning research landscape at MPI-CBG continues to expand the boundaries of deeply quantitative biology. We are tackling emerging challenges: high-dimensional, multi-omic, and spatially and temporally varying datasets, the development of higher-order models for cell dynamics, and the integration of multimodal datasets.
This work, embedded thoroughly within the continually evolving realm of AI, will push the frontiers of our understanding of life through mathematics, data science and machine learning.
