The objective of this research area is the development and application of theoretical methods and data analysis, mathematical modeling and computer simulation techniques for the study of biological systems. We use mathematical and computational methods to address theoretical and experimental biology issues, as defined by the NIH.
Biocomputing is an area of transdisciplinary research, in which the knowledge of the biological area (biology, microbiology, systems biology, genomics, proteomics, etc.) and the analysis and modeling area (mathematics, electrical engineering, computer science, etc. ) are required. The enormous amount of biological data generated experimentally needs to be processed and pattern recognition algorithms are to be applied in order to discover new, unknown and useful ideas to help improve the quality of life.
The algorithms investigated in this area cover the stages of perception in order to obtain relevant information taken from sensors such as video cameras, and the stages of cognition to build models of interpretation for the representation of actions, events and episodes occurred during performed activities. Currently, the main activities analyzed are sports, especially football, but the intention in the near future is to extend our current system to other outdoor sports and other indoor activities to generate statistical, tactical and strategic analysis.
The objective of this research area is the development of computational modeling systems for automated bio-mechanical analysis (cinematic) of human actions, in particular for health improvement and performance optimization. We aim to provide effective and efficient mechanisms for obtaining relevant information to interpret human actions in the interest of coaches, journalists, sports scientists, physiotherapists, doctors, and others.
The objective of this research area is the creation of intelligent systems to allow robots to reason about their environment and enable them to internally simulate their interactions with the world, in order to learn about this process so that they can interact better with it. These intelligent systems provide skills to humanoid robots such as perception, attention, memory and learning, with the aim of solving problems in complex environments, especially in social robotics with human-robot interaction.
This research area focuses on the development and implementation of new efficient algorithms to support the operations of pattern recognition and intelligent systems for research in the laboratory. It takes into account the classic and new approaches to complex computational problems solutions, with the help of numerical methods, algorithmic methods, parallel and distributed computing and scientific visualization of data for exploration.