Integrative Projects
In order to coordinate the research activities of the faculty and students of PPGMMQ, as well as to define the scope of the program, the following integrative projects were outlined during the PPGMMQ Strategic Planning discussion process.
Project 1:
Title: Computational Intelligence and Optimization Techniques for Solving Real-World Problems
Research Lines: Computational Intelligence and Optimization; Quantitative Modeling and Analysis.
Faculty Team: Guilherme de Alencar Barreto (Leader), Bruno Prata, Anselmo Pitombeira, Carlos Diego Rodrigues, Albert Einstein Muritiba, Ricardo Coelho Silva e Jesus Ossian da Cunha Silva.
Description: This research project aims to apply Computational Intelligence and Optimization techniques to solve problems arising from public and private companies. The project focuses on issues related to industry and services in general, such as production in the primary, secondary, and tertiary sectors; transportation; education; energy, among others. Methods for solving such problems include mathematical programming, computer simulation, heuristics and metaheuristics, fuzzy logic, machine learning, among others.
Project 2:
Title: Theoretical Advances and Applications in Statistics and Probabilitys
Research Lines:Quantitative Modeling and Analysis; Computational Intelligence and Optimization.
Faculty Team: Gualberto Montalvo (Leader), Juvêncio Nobre, Charles Cavalcante, José Ailton Alencar, Rafael Farias, Silvia Freitas, André Jalles, Leandro Rêgo, Anselmo Pitombeira, Guilherme Barreto, Giannini Italino e Ronald Nojosa
Descrição: Statistics is a science that can be applied in almost all areas of knowledge, and it adapts and grows daily to try to provide solutions to countless practical and theoretical problems. In this context, Statistics can be seen as an interdisciplinary science that aims to serve as a basis or support for scientific discoveries in different aspects. The theoretical basis of statistical methods is Probability Theory, and understanding the fundamentals of probability is essential for choosing the appropriate methods, whether classical or Bayesian, as well as for the correct interpretation of the results of statistical analyses. Statistics and Probability are very broad areas of science and include subfields such as regression models, multivariate analysis, quality control, experimental design, classification theory, decision theory, game theory, time series, probability distributions, stochastic processes, among others. Many of these areas propose methods that are widely used in the fields of Machine Learning, Artificial Intelligence, and Data Science. The general objective of this project is to construct new theories and methods associated with statistical modeling, inference, optimization, and diagnostics, as well as to apply these methods to solve real-world problems in other areas of science.
Project 3:
Title: Applications of Graphs in Complex Networks and Conflict Analysis
Research Lines: Computational Intelligence and Optimization; Quantitative Modeling and Analysis.
Faculty Team: Leandro Rêgo (Leader), Manoel Campêlo, Carlos Diego Rodrigues, Giannini Italino, Jesus Ossian da Cunha e Ascânio Dias Araújo.
Descrição: Graphs are mathematical structures used to model relationships between pairs of elements within a certain set. With the rapid dissemination and spread of computational social networks, the use of graphs has facilitated the study of various problems in modeling and optimization, closely connected to data science. The analysis of complex networks, particularly social networks, has proven relevant in several contexts, such as investigating behavioral trends, preference analysis, alignment of purposes/ideas, intra-group and inter-group cohesion/tension, and more. Insights derived from this knowledge can be applied to solving a wide range of problems, including team formation, recommendation systems in online stores, political campaigns, patrol systems, and more.
On another front, graphs have also been used to model scenarios and transitions between them in conflict situations, where transitions between scenarios are controlled by the parties involved in the conflict. Through stability analysis, the goal is to identify which scenarios are satisfactory for all parties involved, aiming to resolve conflicts. This project encompasses sub-projects within this overarching theme, addressing more specific problems, including those mentioned above, all with a focus on applying graphs to solve practical problems. The approach involves tools from both humanities (such as psychology, sociology, and anthropology) and exact and technological fields (such as mathematics, statistics, physics, and computing). The intersection of these sub-projects creates an integrative environment that fosters the resolution of the problems studied and promotes collaboration among the researchers involved.
Project 4:
Title: Mathematical Optimization: models, methods, and applications
Research Lines: Computational Intelligence and Optimization; Quantitative Modeling and Analysis.
Faculty Team: Manoel Campêlo (Leader), Rafael Andrade, Albert Muritiba, Anselmo Pitombeira, Bruno Prata, Carlos Diego Rodrigues, Jesus Cunha, Michael Souza, Ricardo Coelho, Nelson Maculan Filho.
Descrição: Mathematical optimization provides valuable tools for solving complex problems aimed at the optimal use of resources and data. By developing optimization models, both theoretical and practical problems are described and solved using mathematical and computational methods. Integrating the complementary skills and experiences of MMQ researchers and their collaborators, this project aims to address various optimization problems, whether theoretical, possibly motivated by applications, or arising directly from real-world scenarios. The following are the general lines of action: (i) proposal, evaluation, and validation of mathematical optimization models for the considered problems or applications; (ii) development, refinement, or adaptation of resolution techniques and algorithms for the studied problems, based on exact, approximate, heuristic, and metaheuristic approaches, as well as decomposition strategies; (iii) evaluation of the computational performance of the proposed methods or their effectiveness in solving real instances; (iv) theoretical study of problem properties that can support other research lines. In this perspective, the aim is to make a vigorous contribution to advancing scientific knowledge in this area while effectively addressing real-world societal problems across various fields of operation.
