The Biomedical Cybernetics Group adopts a transdisciplinary approach integrating information theory, machine learning and network science to investigate adaptive processes that characterize complex interacting systems at different scales, from molecules to organisms, in biology and medicine. This knowledge is leveraged to create novel and more efficient artificial intelligence algorithms; and to perform advanced analysis of patterns hidden in biomedical data, signals and images. Our theoretical effort is to translate advanced mathematical paradigms typically adopted in theoretical physics (like topology and manifold theory) to characterize many-body interactions in quantitative biology. We apply the theoretical frameworks we invent in the mission to develop computational tools for systems and network biology, personalized biomedicine and combinatorial drug therapy.