CV Lattes
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Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica (IMECC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Graduated in Computer Science at the Universidade de São Paulo (1994), Master in Applied Mathematics in the Univerrsidade de São Paulo (1997) and Ph.D. in Applied Mathematics from the sme Unversity. Has experience in Operations Research and Applied Mathematics, focusing on Linear, Nonlinear, Mixing and Dynamic Programming, acting on the following subjects: proximal methods, convex programming, maximal monotone operators and variational inequalities. Coordinator of the National Center for High Performance Computing at São Paulo. (Source: Lattes Curriculum)
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This project deals with theoretical, computational and application aspects of Optimization. The project aims at the development, theoretical analysis, implementation and application of algorithms for different aspects of Optimization, with emphasis on Continuous Optimization. The project relies on applications with which the team is familiar. Emphasis is placed on algorithms with a solid …
The focus of this proposal is the transfer of mathematical knowledge to other areas of science, technology and industry, through a structured research center specially tailored for this purpose. All mathematical knowledge is ultimately applicable, if not directly, through other areas of knowledge. In some areas the application of mathematics is almost immediate. However, the practical imp…
This project concerns the application of the Institutional Technical Reserve of the Centro Nacional de Processamento de Alto Desempenho em São Paulo (CENAPAD-SP/UNICAMP). Its main objective is the acquisition of SSD disks, disk enclosures with SSD adapters, and Infiniband cables to enhance the performance of the storage that supports the supercomputer Ada Lovelace. (AU)
Our group has been active in the Brazilian scientific environment for 40 years, being is sensitive to new trends and modern applications of Optimization. Over the years, this group, funded by successive FAPESP thematic projects, has made significant contributions in areas involving decomposition methods, quasi-Newton methods, sequential quadratic programming, augmented Lagrangian methods,…
(Only some records are available in English at this moment)
The project focuses on proximal gradient methods, seeking to accelerate their convergence through spectral variants. The main objectives include exploring spectral methods for non-convex functions, identifying new competitive applications, and developing techniques that combine spectral strategies with inertia, aiming to optimize the resolution of minimization problems with complex regula…
This project aims to investigate and advance the field of nonconvex composite optimization, focusing on the recent incorporation of second-order information into iterative models. Two recent sources stand out in this area: Forward-backward envelope for the sum of two nonconvex functions: further properties and nonmonotone line-search algorithms [Themeles 2018] and The Indefinite Proximal …
This research project focuses on Difference of Convex (DC) optimization applied to Machine Learning and Data Science. DC optimization, where the objective function is the difference of two convex functions, allows the use of convex analysis techniques and offers a flexible modeling framework. The project aims to investigate specific Machine Learning models, such as Support Vector Machin…
A class of problems of relatively recent interest and of diverse practical applications, yet difficult to analyze and solve, is the minimization of an lower bounded continuously differentiable function over a disjoint block separable set, involving a penalty and/or constraint that result in the sparsity of such blocks. This project aims to explore this theme from the perspective of the re…
Modern convex optimization is experiencing a resurgence, playing a key role in solving the huge new problems of different areas such as Data Science or energy generation with renewable sources. In this project we will study convex optimization from a modern point of view based on Drusvyatskiy's recent monograph ``Convex Analysis and Nonsmooth Optimization''. The aim is to give the student…
Data Science problems have attracted tremendous interest in recent years due to society's unprecedented ability to gather data thatmust be processed.In this context, there has been a resurgence of convex optimization techniques and their extensions, which have been proven to be essential for training of the machine learning models that are fundamental in modern Artificial Intelligence (AI…
(Only some records are available in English at this moment)
The project proposal is to advance operator splitting methods exploring three related fronts, one theoretical and two of practical nature. While not dependent on the theoretical work, the two practical subprojects are designed to take full advantage of any advances obtained in the theoretical portion of the effort. The research topics described below are challenging, at the cutting edge o…
Data science is a very active area that tries to uncover the informationhidden in the vast amount of data collected each day. It can be seen as acombination of techniques from Statistics, Computer Science, and MachineLearning, that results in models that must be optimized using mathematicalalgorithms.In this project, we propose to use state-of-the-art optimization techniques todevelop com…
| 2 / 2 | Ongoing grants |
| 8 / 7 | Completed research grants |
| 3 / 3 | Ongoing scholarships in Brazil |
| 11 / 7 | Completed scholarships in Brazil |
| 3 / 2 | Completed scholarships abroad |
| 27 / 21 | All research grants and scholarships |
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