Areas of Concentration and Research Lines – 2025 onwards
Research Areas
Academic Structure and Research Lines – PPGMMQ
The Graduate Program in Modeling and Quantitative Methods falls within the Interdisciplinary Area of CAPES and is structured around two interrelated concentration areas:
Although the program’s concentration areas and research lines differ in terms of specific purposes, techniques, or applications, there are many similarities and intersections between them. This fosters complementary approaches, enhances the treatment of complex problems, and creates opportunities to obtain appropriate and effective solutions. Generally, researchers from both areas rely on the same foundations and, to some extent, methodologies—modeling, simulation, algorithms—to generate solutions. Together, they can contribute to the advancement of scientific knowledge in these areas and provide the means for companies and organizations to transform data into information, information into insights, and insights into better decisions in today’s competitive environments. By utilizing increasingly powerful technologies, these professionals can delve deeper into real-world scenarios and develop creative solutions to improve daily life.
Data Science
Over the past two decades, we have seen an explosion in the volume of data available on the most diverse aspects of human life. The collection, storage, and processing of this data has become an extremely relevant issue in terms of providing valuable information for dealing with modern problems. The area of data science presents itself exactly in this current and complex scenario, focusing on the discovery of structured information from large databases, which are usually disorganized and also populated by insignificant data, so that they can be useful for making better decisions. The methods employed usually combine statistics and computing for the development of algorithms and mathematical models. The elements of this combination originate the two research lines framed in this concentration area.
📊 Statistical Methods
One of the fundamental bases of Data Science is Statistics. The Statistical Methods research line addresses this aspect of the concentration area, acting from the development of appropriate statistical models for the system or application under study to obtaining and analyzing the solution(s) of these models, in order to assist in solving complex problems, usually affected by numerous variables. Among the most common models are those that provide predictions based on historical series, indicate trends from scenarios, and discriminate/classify new elements, situations, or events based on prior knowledge.
💻 Computational Methods
It is impossible to handle a gigantic volume of data without the use of computing tools. The Computational Methods research line focuses on this aspect of Data Science. This includes several fronts of action, such as: the study of efficient ways to store, organize, access, and visualize large volumes of data; the development of algorithms for manipulating this data and extracting relevant information, treating incorrect or imprecise data, etc. Usually, such data comes from real applications in diverse areas, such as physics, chemistry, biology, engineering, sociology, management, and the financial market.
Operations Research
The driving force behind this concentration area is the development of mathematical models and analytical methods for solving complex problems that appear in decision-making processes. The primary focus is the problem itself, its structure, characteristics, and properties, and not necessarily the data associated with it. The approaches used in this area can be configured with a more theoretical bias, when the main interest lies in describing and studying the problem of interest theoretically and, based on that, proposing solutions that fit its characteristics; or a more applied bias, when the focus is primarily on obtaining a practical solution for the target problem. In any case, they may involve various disciplines, such as mathematical optimization, statistical analysis, queueing theory, stochastic processes, complex network analysis, and are applied in industry, commerce, finance, business, among other sectors. The type of approach and primary focus used define the two research lines of this concentration area.
📐 Optimization
Broadly speaking, this line is devoted to the study and mathematical resolution of problems that consist of choosing, from a set of feasible solutions, the one that is the best according to a comparative criterion. Usually, the process begins with understanding a real problem, which is then translated into one or more generic optimization problems. For the latter, mathematical models are proposed, consisting of maximizing or minimizing a function that ranks the feasible solutions defined by a set of mathematically expressed constraints. The study of the properties of these models, the development and implementation of algorithms to solve them exactly or approximately, the evaluation of the computational efficiency of these algorithms, and their application to obtain solutions to the real problem comprise some of the work possibilities in this research line.
🧠 Decision Support Models
As problems and decision environments become increasingly complex, the decision-making process becomes more dependent on automated support built using science- and technology-based methodologies. This support involves building models and algorithms that can handle voluminous data and complex scenarios. The proposal, analysis, implementation, and use of these tools are among the general topics of this research line. Many problems handled in this line may be optimization problems, and in that case, there is a strong intersection with the approaches common to another line of this same concentration area. In other cases, the treatment of problems uses approaches from other domains, such as decision theory, game theory, probability, conflict analysis, etc.
