We are studying multi-disciplinary approaches for solving decision and optimization problems that merge constraint reasoning, applied mathematics, computational logics, and statistics. The motivations for using hybrid optimization is that real problems present a very complex structure, with side constraints and uncertain data and are composed by distinct, yet tightly connected subproblems. Thus, a single approach is in general ineffective and hybrid solvers are more effective.
Computational Sustainability is an emerging computer science discipline studying techniques for achieving sustainable development. It is an highly interdisciplinary research field encompassing techniques from Constraint Reasoning, Machine Learning, Applied Mathematics, Complex Systems, Soft Computing and Statistics. We focus on optimization and decision support systems for sustainability related applications.
Optimization for Embedded System Design
As the number of processors integrated on a single chip increases with the fast pace dictated by Moore's Law, multicore systems-on-chip (MPSoCs) are becoming truly distributed systems at the micro-scale. From the application viewpoint, requirements for high performance and low power have increased at a breakneck speed in many embedded computing domains like wireless communication, imaging, audio and video processing, graphics, pushed by the demand for higher communication bandwidth, multimedia quality and realistic rendering. For this reason our research effort is being focused on developing methods and tools for efficiently mapping parallel applications onto many-core MPSoC platforms.
Projects and Tools