Advanced computational methods

The development of computational models to solve complex problems requires the use of methods from different computer science areas. This project envision the use of signal processing, computational intelligence, numerical methods and high performance computing to solve problems from the areas of transportation, telecommunications, health, geophysics, agriculture, internet of things, energy, intelligent cities. We seek the development of computationally treatable approaches that are robust and efficient, guaranteeing stability and convergence for the solution of proposed problems.

In the subject of signal processing and computational intelligence, we seek the development of new adaptive filtering algorithms, theoretical analysis of these algorithms and the development of models from observed data. Approaches involving computer vision for embedded devices and techniques capable of reducing energy use on sensor networks are examples of applications.

Regarding numerical methods and high performance computing, the aim is to design and develop computationally efficient approaches to practical and everyday problems, as well as scientific or engineering problems, such as the search for solutions to environmental, geophysical, health, fluid dynamics problems, and models for the financial market. Computational solutions are formulated that are capable of describing the dynamics of such problems in a robust and efficient manner. This includes parallel processing techniques for reducing simulation time through parallel and distributed multicore and multicore processing environments such as supercomputers, clusters, GPUs, and embedded system boards.

Involved Professors

  • Diego Brandão
  • Diego Haddad (Leader)
  • Laura Assis

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