CV Lattes
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Universidade Estadual Paulista (UNESP). Campus de Presidente Prudente. Faculdade de Ciências e Tecnologia (FCT) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Graduated in Mathematics from UNESP (2001) and completed his master's and doctorate degrees in Computational Mathematics from ICMC-USP in 2004 and 2008, respectively, with a sandwich internship at the University of Porto, Portugal. He then completed a postdoctoral fellowship at ICMC-USP in 2009. He currently works as an associate professor in the Department of Mathematics and Computing at FCT-UNESP. He worked on the development of the InfoTracker SP COVID-19 system, which used machine learning techniques to predict the contagion rate throughout the state of São Paulo. Advisor of master's and doctoral students in the postgraduate programs in Computational Mathematics (FCT/UNESP) and Computer Science (UNESP). Principal investigator of FAPESP projects involving Academia and Industry (Center for Mathematical Sciences Applied to Industry Data Science for Smart Industry). He was coordinator of a FAPESP Young Researcher Project, having received an honorable mention from the Odelar Leite Linhares Award in 2009 for the best doctoral thesis from SBMAC, and advisor of two master's dissertations awarded by SBMAC. Associate Editor of the Journal of Computational Mathematics and Data Science and Computational and Applied Mathematics. Deputy Editor of the Applied Mathematics Collection of the Brazilian Mathematical Society. Visiting researcher at the Artificial Intelligence Institute in Dynamic Systems, University of Washington, US between 2022 and 2024. Research interests involve applied mathematics and artificial intelligence. More information: https://lsnia-unesp.github.io/ (Source: Lattes Curriculum)
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One of the most challenging problems coupling interfacial hydrodynamics, soft matterrheology, and material phase change concerns the impact of non-Newtonian drops (polymersolutions, suspensions, emulsions etc.) on a chemically reactive Newtonian liquid inducing dropgelation, i.e., the drop, which is initially a weak gel 1 , solidifies while penetrating the liquid, becomingthen a strong ge…
The Data Science Center for Smart Industry acts as an advanced data analytics hub for industry, aiming to improve operational efficiency, product quality, innovation and sustainability through the application of data science and analytics techniques of data in industrial processes. Contact with industries will be mediated by SENAI and operationalized through workshops and thematic meeting…
Mathematical models allow scientists to translate the behavior of complex systems into a set of equations describing the relation between the key variables. Among different mathematical models, differential equations constitute a powerful tool for modelling the evolution of systems over time, making them suitable to model dynamic phenomena such as population growth, spread of diseases and…
(Only some records are available in English at this moment)
The study of high order numerical methods for transient flows is proposed in this research project. Based on the recent progress obtained by the candidate, it will be developed efficient methodologies to solve technologic and scientific problems which allow the use of different boundary conditions, a wide range of Reynolds numbers and the application of the Newtonian and Viscoelastic flui…
The combination of computational simulations and artificial intelligence tools has been explored in various areas of fluid mechanics, but multiphase flows still require further investigation. In this work, artificial intelligence techniques will be applied to solve problems observed in various industrial processes involving complex multiphase flows, such as oil transportation and processi…
Computational simulations of complex problems governed by differential equations have been significantly improved with advances in Artificial Intelligence (AI) techniques, which can be combined with classical numerical methods. In the current proposal, different AI techniques will be investigated, such as Machine/Deep Learning (ML/DL), Physics Informed Neural Network (PINN), Convolutional…
In this research project, mathematical models adopted in drug-controlled release systems will be investigated. The numerical methods will beapplied to better understand the effectiveness of treatments associated with diseases that affect the retina and choroid. Based on recent work by supervisor and collaborators, including ophthalmologists, the fellow will usean already validated mathema…
In this project we will investigate the least squares method for approximating discrete data. Initially, classic Linear Algebra topics will be analyzed, such as orthogonalization, matrix decompositions, eigenvalues and eigenvectors, etc. After this stage, the student will implement the most known techniques for computationally solving the least squares methods for data approximation. Last…
In this project, we will investigate the numerical computation of interface curvature. The interface will be described in the context of implicit representation framework, particularly focusing on the construction via volume fractions of the Volume-of-Fluid (VoF). Initially, the student will be introduced to the basic concepts of the VoF, as for instance the idea concerning the volume fra…
(Only some records are available in English at this moment)
In this project we will investigate the combination of Sparse Identification Non-linear Dynamics (SINDy) and AutoEncoders (AE) for studying viscoelastic fluid flows. SINDy is a valuable tool for identifying the governing equations of a system based on observational data, while AE can generate reduced-order models that make solving fluid flow problems more computationally efficient. This c…
This proposal sets out a research plan to investigate data-driven, machine learning and numerical methods for dealing with non-Newtonian fluid flows, as for instance, viscoelastic fluids. While the application of machine learning has been already considered as an important tool in fluid mechanics researches for Newtonian fluids, the construction and application of frameworks for non-Newto…
In this project, we will study numerical strategies for dealing with complex topological changes in viscoelastic fluid flows. Based in a combination of the Marker-And-Cell method (MAC) with the front-tracking scheme, we will incorporate the constitutive equations, that describe the viscoelastic flow, into a finite difference in-house code. This code was recently developed for solving axis…
| 4 / 4 | Ongoing grants |
| 2 / 1 | Completed research grants |
| 2 / 2 | Ongoing scholarships in Brazil |
| 16 / 13 | Completed scholarships in Brazil |
| 3 / 3 | Completed scholarships abroad |
| 27 / 23 | All research grants and scholarships |
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