Geometric and combinatorial foundations for emerging information and inference systems [subsparse]
EC-REA Marie Curie Reintegration Grant (subsparse; 268398); Transnational Mobility Award
The promise of emerging information and inference systems lies in their ability to continuously and robustly optimize their performance by intelligently exploiting massive amounts of sensor data in addition to their ability to effectively navigate and coordinate their sensing and computational assets. Despite the advances made, there are crucial challenges, relating to the difficulty of representing disparate, heterogeneous information from diverse sources, as well as rigorous techniques for reasoning about the resulting representations.
Subsparse therefore focuses on two key concepts to obviate these representational and computational bottlenecks: sparsity and submodularity. These two concepts enable the outset and construction of a new range of theory and methods for high-dimensional data. We foresee that our unification of two broad concepts will be an increasingly important research, education and application domain in the future.