CV Lattes
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Ministério da Defesa (Brasil). Instituto Tecnológico de Aeronáutica (ITA). Divisão de Ciência da Computação (IEC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Associate Professor 40h/DE at the Instituto Tecnológico de Aeronáutica (ITA. She holds a PhD from the Graduate Program in Computing and Computational Mathematics at the University of São Paulo, having her thesis funded by FAPESP. From March to October 2020, she was a visiting professor at HEC/Montreal, with BPE/FAPESP. Acted as deputy coordinator of the Graduate Program in Computer Science (2012-2015) and as coordinator of that same program (2016-2019). She is a reviewer of journals such as European Journal of Operational Research, Expert Systems with Applications, Information Sciences, Computers and Operations Research, Computers Operations Research, Applied Soft Computing, IEEE in Evolutionary Computation, among many others. She is associate editor of the journal Expert Systems with Applications and ad hoc consultant at FAPESP, CNPq. He has a research productivity scholarship from CNPq in the area of #8203;#8203;Production Engineering. His research focuses on the area of #8203;#8203;Combinatorial Optimization combining it with Data Science techniques. (Source: Lattes Curriculum)
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This project addresses a wide range of discrete optimization and operations research problems, and aims to contribute with innovative approaches and solutions to production and supply chain management problems. The problems focused in this thematic project are divided into: (A) cutting problems, (B) packing problems, (C) lot-sizing problems, (D) scheduling problems, (E) routing problems (…
The solution of combinatorial optimization problems remains a big challenge for researchers who aim at beyond quality of solutions low CPU time to achieve them. Exact methods are intractable and even the traditional heuristics can not be as efficient as expected for solving large scale instances, relevant to a number of applications. To determine high quality solutions, or even feasible, …
The challenge of finding groups of vertices (or communities) highly connected in a graph (or network), known as the community detection problem in networks or the graph clustering problem, has arouse interest of many physicians, computer scientists, mathematicians, among other researchers in the last decade. The main reasons for such are its wide applicability and, also, due to the emergi…
The community detection problem aims at finding groups of nodes in a graph in such a way that the nodes within communities (or clusters) share high connectivity. The objective of this problem is to find partitions in a network or graph. One of its applications consists in determining communities in social networks in order to explain the behavior of groups of individuals taking their iter…
The increase in greenhouse gas emissions is responsible for the rise in the average global temperature. The rise in temperature causes a number of negative impacts, such as rising sea levels, droughts and damage to crops.One solution to reducing these emissions is to replace combustion vehicles with electric vehicles. However, electric vehicles require more frequent recharging than combus…
The Vehicle Routing Problem (VRP) is a widely studied problem in Operations Research. Over the years, many techniques have been proposed to solve different instances of this problem. Among the techniques, the meta-heuristics (MHs) stand out to provide reasonable solutions in a timely manner as expected by most practical applications. In practice, given the instance of the problem, one alw…
The Storage Location Assignment Problem (SLAP) is a challenge in e-commerce warehouse management, aimed at optimizing item placement to enhance operational efficiency. The growing demand for online shopping has resulted in a significant increase in the number of items that need to be stored and quickly retrieved, making the SLAP even more critical. Efficiently locating items can reduce se…
Anomaly detection in data has a wide variety of applications, such as intrusion detection in computer networks or credit card fraud detection. In particular, if the data can represented by graphs, metrics and methods based on graph theory known for high quality solutions can be used. In the case of static graphs, the anomaly detection problem has been extensively studied and several algor…
Graph clustering strategies are useful for pattern recognition in large scale relational data, for example. In particular, airline ticket data can be modeled as oriented graphs (digraph). However, for better representation of these data, one must take into account the changes in costs and tickets availability. Thus, clustering these graphs that change over time, known as dynamic graphs, a…
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Deep learning (DL) has been successfully applied to a variety of signal and information processing tasks with outstanding results. It consists of computational models composed by multiple processing layers whose goal is to learn data representation considering multiple levels of abstraction. Several recent attempts to address combinatorial optimization problems (COPs) by deep networks can…
The sustainable supply chain problem can be represented by mathematical optimization models. The planning of these chains, in this project, aims at minimizing costs and the environmental impact and maximizing social criteria. In this way, this project considers a sustainable supply chain multiobjective optimization problem. In the literature, the developed models and methods are evaluate…
| 1 / 1 | Ongoing grants |
| 3 / 3 | Completed research grants |
| 3 / 3 | Ongoing scholarships in Brazil |
| 14 / 10 | Completed scholarships in Brazil |
| 2 / 2 | Completed scholarships abroad |
| 24 / 20 | All research grants and scholarships |
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