UFABC-professores

Ronaldo Cristiano Prati

Possui graduação em Bacharelado em Ciência da Computação pela Universidade de São Paulo (2001), mestrado em Ciências da Computação e Matemática Computacional pela Universidade de São Paulo (2003) e doutorado em Ciências da Computação e Matemática Computacional pela Universidade de São Paulo (2006). Atualmente é professor adjunto da Universidade Federal do ABC, onde coordenou o programa de pós-graduação em Ciência da Computação de fevereiro de 2011 a março de 2015, elaborando a proposta de criação do mestrado (2011) e doutorado (2015) do programa. Tem experiência na área de Ciência da Computação, com ênfase em Inteligência Artificial, atuando principalmente nos seguintes temas: descoberta de conhecimento, aprendizado de máquina, mineração de dados, tratamento de classes desbalanceadas e inteligência artificial. (Texto informado pelo autor)

  • http://lattes.cnpq.br/7851650523179414 (23/08/2024)
  • Rótulo/Grupo: CMCC
  • Bolsa CNPq:
  • Período de análise: 2009-HOJE
  • Endereço: Universidade Federal do ABC, Centro de Matemática, Computação e Cognição. Rua Santa Adélia, 166 Bangú 09210170 - Santo André, SP - Brasil - Caixa-postal: 668 Telefone: (11) 49968309
  • Grande área: Ciências Exatas e da Terra
  • Área: Ciência da Computação
  • Citações: Google Acadêmico

Produção bibliográfica

Produção técnica

Produção artística

Orientações em andamento

Supervisões e orientações concluídas

Projetos de pesquisa

Prêmios e títulos

Participação em eventos

Organização de eventos

Lista de colaborações

Produção bibliográfica

Produção técnica

Produção artística

Orientações em andamento

Supervisões e orientações concluídas

Projetos de pesquisa

  • Total de projetos de pesquisa (3)
    1. 2018-Atual. Computational Material Science and Chemistry
      Descrição: Much of the energy consumed in the world comes from the burning of petroleum-based fossil fuels (oil diesel, gasoline, kerosene for aviation, and liquefied gas). Beyond the fuels to run our modern society, petroleum is also the raw material of an extensive chain of products, e.g., paraffin, asphalt products, petrochemical naphtha, solvents, plastics, etc. Despite the great importance of fossil fuels, there are several reasons for the research of renewable and new alternative energy sources, which includes environmental problems and the fact that petroleum will exhaust in the future. Therefore, there is a great interest in the use of renewable energy resources such as hydroelectric, biomass, offshore/inland wind, photovoltaics, hydrogen, as well as the development of new energy carriers from the conversion of methane or/and from CO2 captured from ar combined with H2 from water splitting. Due to the large demand, we believe that those technologies will be combined in future to yield a sustainable planet with net-zero emissions, however, our Computational Material Science and Chemistry (CMSC) Division will focus only on few of those pathways. We believe strongly that among all those energy sources, four pathways should be followed, namely, $(i)$ photons to electrons using photovoltaics devices as photovoltaics is expected to contribute with about \SI{30}{\percent} (Shell report) for our energy matrix in future, $(ii)$ energy storage using batteries and ultra(super)capacitors to support inland/offshore wind energy growth, $(iii)$ CO2 capture and conversion to value-added products -- methanol, gasoline-range hydrocarbons, which can provide a crucial role to our future as an environmental problem can be part of the solution, $(iv)$ methane conversion to high density liquid carriers -- methanol, etc, which is expected to play an important role due the large supply of natural gas world-wide. To address those problems, we propose to employ the state-of-the-art in ComputationalMaterial Science tools to deliver cutting-edge solutions. For practical organization, we propose nine projects, which includes two projects for photons to electrons conversion based on photovoltaics (in particular perovskites) and photochemistry, two projects to study the conversion of methane and CO2 captured from air to high-value products such as high density energy carriers (methanol) employing porous materials combined with transition-metal finite size particles. For energy storage, we explore batteries and ultra(super)capacitors, in particular, we are interested in beyond-Li technologies and employing cheap ionic conductors. Beyond of the six projects, we proposed three cross-boundary projects with the potential to contribute to the previous six projects, as well as to provide their on solo contribution, which includes: nanocatalysts engineering, fluid dynamics to address the flow of ionic liquids, and machine learning techniques, which will be applied to explore theoretical and experimental data from the remaining three Divisions. Therefore, our Division combined with the additional three Center Divisions have a great potential to yield important contributions to the generation of New Energies.. Situação: Em andamento; Natureza: Pesquisa. Alunos envolvidos: Graduação: (2) / Mestrado acadêmico: (3) / Mestrado profissional: (0) / Doutorado: (5) . Integrantes: Ronaldo Cristiano Prati - Coordenador / André Carlos Ponce de Leon Ferreira de Carvalho - Integrante / Gustavo Enrique Almeida Prado Alves Batista - Integrante / Juarez Lopes Ferreira da Silva - Integrante. Financiador(es): Fundação de Amparo à Pesquisa do Estado de São Paulo - Auxílio financeiro / BG E & P Brasil - Auxílio financeiro.
      Membro: Ronaldo Cristiano Prati.
    2. 2017-Atual. Intelligent Traps and Sensors: an Innovative Approach to Control Insect Pests and Disease Vectors
      Descrição: Insects are undoubtedly important to agriculture, the environment and human health. Many insect species are beneficial to the environment and humans. For example, insects are responsible for pollinating at least two-thirds of all food consumed in the world. Due to its importance to humans, the recent decline in populations of pollinator insects, especially bees, is considered a serious environmental problem; frequently associated with pesticide exposure. In contrast, insect pests destroy over 40 billion U.S. dollars worth of food each year and vectors are responsible for spreading diseases that kill over one million people annually, such as malaria, dengue and chikungunya fevers and zika virus. In this project, we propose an intelligent trap that captures harmful insect species. Such a trap uses a sensor that we have developed over the last years to automatically recognize insect species using wingbeat data. The insect recognition will allow the creation of real-time insect density maps that can be used to support local interventions. For instance, in the case of insect pests, these maps will allow more local use of insecticides and, therefore, a reduced impact over the environment. In the case of disease vectors, this trap will make some sophisticated but highly costly interventions, such as SIT (Sterile Insect Technique), more cost-effective. In this project, we show how this real application can expand the limits of the state-of-the-art research in Computer Science, particularly in Machine Learning and Data Stream Mining areas. In order to demonstrate the practical aspects of our proposal, we will concentrate in the identification of two species: the Asian citrus psyllid, vector of greening, a terrible citrus disease and the Aedes aegypti vector of dengue, chikungunya and yellow fevers, as well as, the zika virus, recently associated with cases of microcephaly in newborns.. Situação: Em andamento; Natureza: Pesquisa. Alunos envolvidos: Mestrado acadêmico: (2) Doutorado: (2) . Integrantes: Ronaldo Cristiano Prati - Integrante / Gustavo Enrique Almeida Prado Alves Batista - Coordenador. Financiador(es): Fundação de Amparo à Pesquisa do Estado de São Paulo - Auxílio financeiro.
      Membro: Ronaldo Cristiano Prati.
    3. 2017-Atual. Smart Water Management Platform (SWAMP)
      Descrição: The SWAMP project develops IoT based methods and approaches for smart water management in precision irrigation domain, and pilots them in Italy, Spain, and Brazil (2). Water is vital for ensuring food security to the world?s population, and agriculture is the biggest consumer amounting for 70% of freshwater. The water wastages are caused mainly by leakages in distribution and irrigation systems, and in the field application methods. The most common technique, surface irrigation wastes a high percentage of the water by wetting areas where no plants benefit from it. Localized irrigation can use water more efficiently and effectively, avoiding both under-irrigation and over-irrigation. However, in an attempt to avoid under-irrigation, farmers feed more water than is needed resulting not only to productivity losses, but also water is wasted. Therefore, technology should be developed and deployed for sensing the level of water needed by the plantation and for flowing the water to places where and when needed. The SWAMP project addresses these issues by use of the Internet of Things (IoT), data analytics, autonomous devices and other related technologies. The challenges addressed by SWAMP project are following: 1) Reducing effort in software development for IoT-based smart applications. 2) Automating advanced platforms and integrating different technologies and components. 3) The integration of heterogeneous and advanced sensors, particularly flying sensors (drones) providing precision in the water supply for irrigation. 4) The use of a Software Platform together with technologies such as IoT, Big Data, Cloud/Fog and drones for the deployment of pilot applications for smart water management. 5) Proposing, testing and validating new business models for using IoT in smart water management settings. 6) Technological components must be flexible and adaptable enough in order to adapt to different contexts and to be replicable to different locations and contexts.. Situação: Em andamento; Natureza: Pesquisa. Alunos envolvidos: Graduação: (2) / Especialização: (0) / Mestrado acadêmico: (4) / Mestrado profissional: (0) / Doutorado: (4) . Integrantes: Ronaldo Cristiano Prati - Integrante / Carlos Alberto Kamienski - Coordenador / João Henrique Kleinschmidt - Integrante. Financiador(es): Rede Nacional de Ensino e Pesquisa - Auxílio financeiro / Comissão Européia - Auxílio financeiro.
      Membro: Ronaldo Cristiano Prati.
      Descrição: The SWAMP project develops IoT based methods and approaches for smart water management in precision irrigation domain, and pilots them in Italy, Spain, and Brazil (2). Water is vital for ensuring food security to the world´s population, and agriculture is the biggest consumer amounting for 70% of freshwater. The water wastages are caused mainly by leakages in distribution and irrigation systems, and in the field application methods. The most common technique, surface irrigation wastes a high percentage of the water by wetting areas where no plants benefit from it. Localized irrigation can use water more efficiently and effectively, avoiding both under-irrigation and over-irrigation. However, in an attempt to avoid under-irrigation, farmers feed more water than is needed resulting not only to productivity losses, but also water is wasted. Therefore, technology should be developed and deployed for sensing the level of water needed by the plantation and for flowing the water to places where and when needed. The SWAMP project addresses these issues by use of the Internet of Things (IoT), data analytics, autonomous devices and other related technologies. The challenges addressed by SWAMP project are following: 1) Reducing effort in software development for IoT-based smart applications. 2) Automating advanced platforms and integrating different technologies and components. 3) The integration of heterogeneous and advanced sensors, particularly flying sensors (drones) providing precision in the water supply for irrigation. 4) The use of a Software Platform together with technologies such as IoT, Big Data, Cloud/Fog and drones for the deployment of pilot applications for smart water management. 5) Proposing, testing and validating new business models for using IoT in smart water management settings. 6) Technological components must be flexible and adaptable enough in order to adapt to different contexts and to be replicable to different locations and contexts.. Situação: Concluído; Natureza: Pesquisa. Alunos envolvidos: / Mestrado profissional: (2) / Doutorado: (5) . Integrantes: João Henrique Kleinschmidt - Integrante / Carlos Alberto Kamienski - Coordenador / Ronaldo Cristiano Prati - Integrante. Financiador(es): European Comission - Auxílio financeiro / Rede Nacional de Ensino e Pesquisa - Auxílio financeiro.
      Membro: João Henrique Kleinschmidt.
      Descrição: The SWAMP project develops IoT based methods and approaches for smart water management in precision irrigation domain, and pilots them in Italy, Spain, and Brazil (2). Water is vital for ensuring food security to the world?s population, and agriculture is the biggest consumer amounting for 70% of freshwater. The water wastages are caused mainly by leakages in distribution and irrigation systems, and in the field application methods. The most common technique, surface irrigation wastes a high percentage of the water by wetting areas where no plants benefit from it. Localized irrigation can use water more efficiently and effectively, avoiding both under-irrigation and over-irrigation. However, in an attempt to avoid under-irrigation, farmers feed more water than is needed resulting not only to productivity losses, but also water is wasted. Therefore, technology should be developed and deployed for sensing the level of water needed by the plantation and for flowing the water to places where and when needed. The SWAMP project addresses these issues by use of the Internet of Things (IoT), data analytics, autonomous devices and other related technologies. The challenges addressed by SWAMP project are following: 1) Reducing effort in software development for IoT-based smart applications. 2) Automating advanced platforms and integrating different technologies and components. 3) The integration of heterogeneous and advanced sensors, particularly flying sensors (drones) providing precision in the water supply for irrigation. 4) The use of a Software Platform together with technologies such as IoT, Big Data, Cloud/Fog and drones for the deployment of pilot applications for smart water management. 5) Proposing, testing and validating new business models for using IoT in smart water management settings. 6) Technological components must be flexible and adaptable enough in order to adapt to different contexts and to be replicable to different locations and contexts. http://swamp-project.org/. Situação: Concluído; Natureza: Pesquisa. Alunos envolvidos: Mestrado acadêmico: (2) Doutorado: (2) . Integrantes: Carlos Alberto Kamienski - Coordenador / Stenio Fernandes - Integrante / João Henrique Kleinschmidt - Integrante / Ronaldo Cristiano Prati - Integrante / Alexandre Heideker - Integrante / Ivan Zyrianoff - Integrante / Rodrigo Filev Maia - Integrante / Andre Torre Neto - Integrante / Luis Henrique Bassoi - Integrante / Marcos Cezar Visoli - Integrante. Financiador(es): Rede Nacional de Ensino e Pesquisa - Auxílio financeiro / European Comission - Auxílio financeiro.
      Membro: Carlos Alberto Kamienski.

Prêmios e títulos

  • Total de prêmios e títulos (4)
    1. Melhores trabalhos do MBA em Inteligência Artificial e Big Data - 1a edição (Hussein Hassem Sampaio El Messmar), ICMC/USP São Carlos.. 2022.
      Membro: Ronaldo Cristiano Prati.
    2. 2o. Best Poster AWARD - 3rd Cine Conference, Center for Innovation on New Energies (CINE).. 2022.
      Membro: Ronaldo Cristiano Prati.
    3. Best Paper Award - IEEE International Workshop on Metrology for Agriculture and Forestry, Universistat di Trento.. 2020.
      Membro: Ronaldo Cristiano Prati.
    4. Melhor trabalho - II Encontro de Iniciação Científica - Eixo Comunicação e Informação (ALUNO: GLEISON MORAIS - ORIENTADOR: RONALDO PRATI), Universidade Federal do ABC.. 2012.
      Membro: Ronaldo Cristiano Prati.

Participação em eventos

  • Total de participação em eventos (2)
    1. Encontro Nacional de Inteligência Artificial.Combinando métodos de seleção de atributos usando agregação de rankings. 2009. (Encontro).
    2. II International Workshop on Web and Text Intelligence (WTI - 2009).Utilizando Co-Training para Realimentação de Relevância na WEB. 2009. (Oficina).

Organização de eventos

  • Total de organização de eventos (2)
    1. Cristiano Prati, Ronaldo; Dimuro, G. ; CAMPOS, A. M. C.. VIII Encontro Nacional de Inteligência Artificial. 2011. Congresso
    2. Lorena, A. C. ; ROZANTE, L. C. S. ; LIMA, M. F. S. ; TABOAS, P. Z. ; PRATI, R. C. ; Yossi Zana. Semana do Centro de Matemática, Computação e Cognição. 2009. Outro

Lista de colaborações

  • Colaborações endôgenas (6)
    • Ronaldo Cristiano Prati ⇔ Carlos Alberto Kamienski (8.0)
      1. TOGNERI, RODRIGO ; PRATI, RONALDO ; NAGANO, HITOSHI ; KAMIENSKI, CARLOS. Data-driven water need estimation for IoT-based smart irrigation: A survey. EXPERT SYSTEMS WITH APPLICATIONS. v. 225, p. 120194, issn: 0957-4174, 2023.
      2. RIBEIRO JUNIOR, FRANKLIN M. ; BIANCHI, REINALDO A.C. ; Prati, Ronaldo C. ; KOLEHMAINEN, KARI ; SOININEN, JUHA-PEKKA ; KAMIENSKI, CARLOS A.. Data reduction based on machine learning algorithms for fog computing in IoT smart agriculture. BIOSYSTEMS ENGINEERING. v. 223, p. 142-158, issn: 1537-5110, 2022.
      3. TOGNERI, RODRIGO ; FELIPE DOS SANTOS, DIEGO ; CAMPONOGARA, GLAUBER ; NAGANO, HITOSHI ; CUSTÓDIO, GILLIARD ; PRATI, RONALDO ; FERNANDES, STÊNIO ; KAMIENSKI, CARLOS. Soil Moisture Forecast for Smart Irrigation: The Primetime for Machine Learning. EXPERT SYSTEMS WITH APPLICATIONS. v. 207, p. 117653, issn: 0957-4174, 2022.
      4. BIONDI, GABRIELA ; PRATI, RONALDO ; BORELLI, FABRIZIO ; OTTOLINI, DENER ; DE OLIVEIRA, NELSON GONÇALVES ; KAMIENSKI, CARLOS. IoTracker: A probabilistic event tracking approach for data-intensive IoT smart applications. Internet of Things. v. 19, p. 100556, issn: 2542-6605, 2022.
      5. TOGNERI, RODRIGO ; KAMIENSKI, CARLOS ; DANTAS, RAMIDE ; PRATI, RONALDO ; TOSCANO, ATTILIO ; SOININEN, JUHA-PEKKA ; CONIC, TULLIO SALMON. Advancing IoT-Based Smart Irrigation. IEEE Internet of Things Magazine. v. 2, p. 20-25, issn: 2576-3180, 2019.
      6. PRATI, RONALDO C. ; BORELLI, FABRIZIO ; ZYRIANOFF, IVAN DIMITRY ; SILVA, DENER ; TOGNERI, RODRIGO ; Kamienski, Carlos. IrrigaSim: An Irrigation Simulation, Processing, and Animation Environment. Em: 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), p. 305, 2021.
      7. RIBEIRO, FRANKLIN MAGALHAES ; PRATI, RONALDO ; BIANCHI, REINALDO ; KAMIENSKI, CARLOS. A Nearest Neighbors based Data Filter for Fog Computing in IoT Smart Agriculture. Em: 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), p. 63, 2020.
      8. KAMIENSKI, CARLOS ; Cristiano Prati, Ronaldo ; KLEINSCHMIDT, J. H. ; SOININEN, JUHA-PEKKA. Designing an Open IoT Ecosystem. Em: Workshop on Cloud Networks (WCN 2019), v. 1, 2019.

    • Ronaldo Cristiano Prati ⇔ João Paulo Gois (3.0)
      1. BATAGELO, H. C. ; GOIS, J. P. ; BUENO, L. R. ; ROZANTE, L. C. S. ; PRATI, R. C.. Lógica de programação: Variáveis e estruturas sequenciais. Em: Maria ds Graças Bruno Mrietto; Mário Minami; Pieter Willem Westera. (Org.). Bases Computacionais da Ciência. 1ed.Belo Horizonte (MG). : Fino Traço Editora. 2013.p. 143-160.
      2. BATAGELO, H. C. ; GOIS, J. P. ; BUENO, L. R. ; ROZANTE, L. C. S. ; PRATI, R. C.. Lógica de programação: Estruturas condicionais. Em: Maria das Graças Bruno Marietto, Mário Minami, Pieter Willem Westera. (Org.). Bases Computacionais da Ciência. 1ed.Santo André-SP. : Belo Horizonte (MG). 2013.p. 161-174.
      3. BATAGELO, H. C. ; GOIS, J. P. ; BUENO, L. R. ; ROZANTE, L. C. S. ; PRATI, R. C.. Lógica de programação: Estruturas de repetição. Em: Maria das Graças Bruno Marietto, Mário Minami, Pieter Willem Westera. (Org.). Bases Computacionais da Ciência. 1ed.Santo André-SP. : Belo Horizonte (MG). 2013.p. 175-184.

    • Ronaldo Cristiano Prati ⇔ Luiz Carlos da Silva Rozante (3.0)
      1. BATAGELO, H. C. ; GOIS, J. P. ; BUENO, L. R. ; ROZANTE, L. C. S. ; PRATI, R. C.. Lógica de programação: Variáveis e estruturas sequenciais. Em: Maria ds Graças Bruno Mrietto; Mário Minami; Pieter Willem Westera. (Org.). Bases Computacionais da Ciência. 1ed.Belo Horizonte (MG). : Fino Traço Editora. 2013.p. 143-160.
      2. BATAGELO, H. C. ; GOIS, J. P. ; BUENO, L. R. ; ROZANTE, L. C. S. ; PRATI, R. C.. Lógica de programação: Estruturas condicionais. Em: Maria das Graças Bruno Marietto, Mário Minami, Pieter Willem Westera. (Org.). Bases Computacionais da Ciência. 1ed.Santo André-SP. : Belo Horizonte (MG). 2013.p. 161-174.
      3. BATAGELO, H. C. ; GOIS, J. P. ; BUENO, L. R. ; ROZANTE, L. C. S. ; PRATI, R. C.. Lógica de programação: Estruturas de repetição. Em: Maria das Graças Bruno Marietto, Mário Minami, Pieter Willem Westera. (Org.). Bases Computacionais da Ciência. 1ed.Santo André-SP. : Belo Horizonte (MG). 2013.p. 175-184.

    • Ronaldo Cristiano Prati ⇔ João Henrique Kleinschmidt (2.0)
      1. KAMIENSKI, CARLOS ; Cristiano Prati, Ronaldo ; KLEINSCHMIDT, J. H. ; SOININEN, JUHA-PEKKA. Designing an Open IoT Ecosystem. Em: Workshop on Cloud Networks (WCN 2019), v. 1, 2019.
      2. SOUTO, S. P. ; Prati, Ronaldo Cristiano ; KLEINSCHMIDT, J. H.. Otimização multiobjetivo de trajetórias de VANTs utilizando curvas de Bézier e Algoritmos Genéticos. Em: XIV Brazilian Congress of Computational Intelligence, v. 1, 2019.

    • Ronaldo Cristiano Prati ⇔ Ana Melva Champi Farfán (1.0)
      1. CHALLHUA, RONALDO ; PRATI, RONALDO ; CHAMPI, ANA. Feature engineering and machine learning for electrochemical detection of rabies virus in graphene-based biosensors. MICROCHEMICAL JOURNAL. v. 204, p. 111074, issn: 0026-265X, 2024.

    • Ronaldo Cristiano Prati ⇔ Fabricio Olivetti de França (1.0)
      1. PRATI, RONALDO C. ; DE FRANCA, FABRICIO OLIVETTI. Extending features for multilabel classification with swarm biclustering. Em: 2013 IEEE Congress on Evolutionary Computation (CEC), p. 2964, 2013.




Data de processamento: 16/11/2024 16:24:34