Algorithms, Optimization, and Computational Modeling
This area involves the development of theoretical and computational models, as well as efficient algorithms for the analysis, understanding, and description of complex problems in the areas of environmental, biological, human and engineering sciences, among others.
A computational model can have a discrete or continuous, deterministic or probabilistic character, besides being able to involve a high number of variables to be analyzed. From a model, efficient methods are developed based on computational techniques and the computational complexity analysis of these methods can also be performed.
As far as the algorithmic part is concerned, the development of models and algorithms through graph structures (essential elements in several areas of applied computation), as well as a theoretical analysis of the complexity of the proposed solution is desired. Applications of the algorithms developed in the areas of Information Security, Bioinformatics, Computational Geometry, and Adaptive Filtering are also focus of study, as well as the analysis of matrix properties of linear algebra problems.
In the context of optimization, it is sought to find the best solution among several possible solutions, in order to maximize (or minimize) an objective function which guides the search for good solutions. Usually, it is intended to achieve maximization of results associated with resource minimization. In this sense, research includes the development of exact algorithms, such as those based on linear programming and integer programming, as well as heuristics. We also see the development of combinations of exact and heuristic methods, seeking the best practical solution to the problems addressed. Practical problems in this area involve the energy sector, traffic control, content distribution in computational clouds, among others.
Computational modeling deals with problems involving the resolution of differential equations using numerical methods, inverse problem techniques, and discrete models. We are looking for the development of models robust enough to describe the problem addressed, as well as computationally efficient methods, either for memory savings or for greater computational efficiency through models in parallel and distributed computing, including GPU architectures.
The multidisciplinary nature of this line allows the researcher to acquire a solid and broad training due to the applicability in several areas and the knowledge of the various facets of the problem addressed.
Data Management and Applications
The Data Management and Applications area encompass both the aspects related to the data structure, data storage, and location as well as the process of producing information and extracting knowledge from the data. Research in this area involves data structures and algorithms to support the data knowledge extraction process, as well as in the application and development of functions for preprocessing, machine learning and post-processing of data. In this area, it is appropriate to establish a datacentric treatment for those processes which, commonly, correspond to in-silico experiments.
This research area is dependent on the addressed problem domain and has a strong multidisciplinary character. The application of this research area are commonly observed in the government, scientific and business areas. In the government area, one can highlight applications in the areas of planning, health, transportation, education, and communication. In the scientific area, applications in astronomy, seismic and drugs stand out. In the business area, we highlight the development of software for the Information Technology market and its applications in marketing, social networks, multimedia systems, and smart cities. All these domains bring with them different technologies present in the data production and consumption area that can be exemplified from the Internet of Things (IoT), sensor networks to interactive applications.
In this flood of data, applications particular features and demands bring numerous challenges in managing large volumes of data. Regarding large volumes of data processing, there is also a pressing need to use high-performance processing (PAD) to achieve large-scale data analysis. There are significant challenges in the establishment of these analyzes, usually modeled as workflows. In these workflows, activities, and data are directed to execution in some PAD environment (e.g., clusters, grids, clouds).
The different applications, while presenting themselves as applied research, often provide the opportunity to elaborate new theoretical frameworks in basic research, of a more general character, for the solution of these practical problems. Basic research occurs both in establishing representations that are agnostic to the medium in which they will be performed, as well as in the elaboration of new functions that are more appropriate to the different data sources and that have a broad spectrum of use.