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Diogo R. Ferreira is an associate professor of information systems at IST, University of Lisbon, and a researcher affiliated with the Institute for Plasmas and Nuclear Fusion (IPFN) in Portugal. Since 2014, he has been applying deep learning and GPU computing to the analysis and processing of fusion data from plasma diagnostics, especially at the Joint European Torus (JET) and other EUROfusion devices. His work has been highlighted at the Nvidia GPU Technology Conference and has been published in journals such as Plasma Physics and Controlled Fusion, and Machine Learning: Science and Technology. These often serve as references for other applications of machine learning in the field of plasma physics. He is the author of two books and has supervised 2 PhD students and 30+ MSc students.
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Identificação

Identificação pessoal

Nome completo
DIOGO MANUEL RIBEIRO FERREIRA

Nomes de citação

  • Diogo R. Ferreira
  • D. R. Ferreira

Identificadores de autor

Ciência ID
061C-AE88-DC66
ORCID iD
0000-0001-5818-9406
Google Scholar ID
CSEqKy8AAAAJ

Endereços de correio eletrónico

  • diogo.ferreira@tecnico.ulisboa.pt (Profissional)

Telefones

Telefone
  • 214233552 (Profissional)

Moradas

  • Instituto Superior Técnico, Campus do Taguspark, Avenida Prof. Dr. Cavaco Silva, 2744-016, Porto Salvo, Oeiras, Portugal (Profissional)

Websites

Domínios de atuação

  • Ciências Exatas - Ciências da Computação e da Informação
  • Ciências Exatas - Física - Física dos Fluídos e dos Plasmas

Idiomas

Idioma Conversação Leitura Escrita Compreensão Peer-review
Inglês Utilizador proficiente (C2) Utilizador proficiente (C2) Utilizador proficiente (C2) Utilizador proficiente (C2) Utilizador proficiente (C2)
Francês Utilizador elementar (A2) Utilizador elementar (A2) Utilizador elementar (A2) Utilizador elementar (A2) Utilizador elementar (A2)
Alemão Utilizador elementar (A2) Utilizador elementar (A2) Utilizador elementar (A2) Utilizador elementar (A2) Utilizador elementar (A2)
Sueco Utilizador elementar (A2) Utilizador elementar (A2) Utilizador elementar (A2) Utilizador elementar (A2) Utilizador elementar (A2)
Formação
Grau Classificação
2000/09/01 - 2004/02/17
Concluído
 Engenharia Electrotécnica e de Computadores (Doutoramento)
Universidade do Porto Faculdade de Engenharia, Portugal
"Workflow Management Systems Supporting the Engineering of Business Networks" (TESE/DISSERTAÇÃO)
1994/09/01 - 1999/07/31
Concluído
 Engenharia Electrotécnica e de Computadores (Licenciatura)
Especialização em Automação, Controlo e Instrumentação
Universidade do Porto Faculdade de Engenharia, Portugal
Percurso profissional

Docência no Ensino Superior

Categoria Profissional
Instituição de acolhimento 		Empregador
2023/09 - Atual Professor Associado (Docente Universitário) Universidade de Lisboa Instituto Superior Técnico, Portugal
2006/02 - 2023/08 Professor Auxiliar (Docente Universitário) Universidade de Lisboa Instituto Superior Técnico, Portugal
2004/05 - 2006/02 Professor Auxiliar Convidado (Docente Universitário) Universidade do Porto Faculdade de Engenharia, Portugal
2003/09 - 2004/05 Assistente Convidado (Docente Universitário) Universidade do Porto Faculdade de Engenharia, Portugal
Projetos

Projeto

Designação Financiadores
2013 - 2015 PADSTEP
24724
Investigador
Instituto de Engenharia de Sistemas e Computadores Novas Tecnologias, Portugal
 Quadro de Referência Estratégico Nacional
Concluído
2007 - 2010 COLABORATÓRIO
PTDC/CCI/70512/2006
Investigador
Instituto de Engenharia de Sistemas e Computadores Novas Tecnologias, Portugal
 Fundação para a Ciência e a Tecnologia
Concluído
2008 - 2009 UPCASE
UPCASE
Investigador
Universidade de Lisboa Instituto Superior Técnico, Portugal
 PT Inovação
Concluído
2000 - 2002 DAMASCOS
IST-1999-11850
Investigador
Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciência, Portugal
 European Commission Fifth Framework Programme for Research and Development
Concluído
1999 - 2001 PRONEGI
IC-PME-PRONEGI
Investigador
Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciência, Portugal
 Agência Nacional de Inovação SA
Concluído

Outro

Designação Financiadores
2019/08 - 2020/02 DELADIS
DELADIS
Investigador responsável
Instituto de Plasmas e Fusão Nuclear, Portugal
 European Consortium for the Development of Fusion Energy
Concluído
Produções

Publicações

Artigo em conferência
  1. "Assessing Agile Software Development Processes with Process Mining: A Case Study". 2018.
    10.1109/CBI.2018.00021
  2. Andre C. Santos; Pedro C. Diniz; Joao M.P. Cardoso; Diogo R. Ferreira. "A Domain-Specific Language for the Specification of Adaptable Context Inference". 2011.
    10.1109/euc.2011.4
  3. Daniel Gillblad; Rebecca Steinert; Diogo R. Ferreira. "Estimating the Parameters of Randomly Interleaved Markov Models". 2009.
    10.1109/icdmw.2009.17
Artigo em revista
  1. "Explainable deep learning for the analysis of MHD spectrograms in nuclear fusion". Machine Learning: Science and Technology 3 1 (2022): 015015-015015. http://dx.doi.org/10.1088/2632-2153/ac44aa.
    10.1088/2632-2153/ac44aa
  2. "Investigating the physics of disruptions with real-time tomography at JET". Plasma Science and Technology (2021): http://dx.doi.org/10.1088/2058-6272/ac3ba4.
    10.1088/2058-6272/ac3ba4
  3. "A brief comment on the numerical results in 'Electrostatic potential of a uniformly charged triangle in barycentric coordinates'". European Journal of Physics 42 6 (2021): 068001-068001. http://dx.doi.org/10.1088/1361-6404/ac1b72.
    10.1088/1361-6404/ac1b72
  4. "Using HPC infrastructures for deep learning applications in fusion research". Plasma Physics and Controlled Fusion 63 8 (2021): 084006-084006. http://dx.doi.org/10.1088/1361-6587/ac0a3b.
    10.1088/1361-6587/ac0a3b
  5. "Progress in preparing real-time control schemes for Deuterium-Tritium operation in JET". Fusion Engineering and Design 166 (2021): 112305-112305. http://dx.doi.org/10.1016/j.fusengdes.2021.112305.
    10.1016/j.fusengdes.2021.112305
  6. "Influences of heating and plasma density on impurity production and transport during the ramp-down phase of JET ILW discharge". Plasma Physics and Controlled Fusion 63 3 (2021): 035008-035008. https://doi.org/10.1088/1361-6587/abd13c.
    10.1088/1361-6587/abd13c
  7. "Monitoring the plasma radiation profile with real-time bolometer tomography at JET". Fusion Engineering and Design 164 (2021): 112179-112179. https://www.sciencedirect.com/science/article/pii/S0920379620307274.
    https://doi.org/10.1016/j.fusengdes.2020.112179
  8. "Onset of tearing modes in plasma termination on JET: the role of temperature hollowing and edge cooling". Nuclear Fusion (2021): http://iopscience.iop.org/article/10.1088/1741-4326/abe3c7.
  9. "Assessment of tomography signals in view of neural network reconstruction at ISTTOK". Journal of Instrumentation (2020): http://dx.doi.org/10.1088/1748-0221/15/01/c01022.
    10.1088/1748-0221/15/01/c01022
  10. "Deep Learning for Plasma Tomography and Disruption Prediction From Bolometer Data". IEEE Transactions on Plasma Science 48 1 (2020): 36-45. http://dx.doi.org/10.1109/tps.2019.2947304.
    10.1109/TPS.2019.2947304
  11. "Deep Learning for the Analysis of Disruption Precursors Based on Plasma Tomography". Fusion Science and Technology 76 8 (2020): 901-911. https://doi.org/10.1080/15361055.2020.1820749.
    10.1080/15361055.2020.1820749
  12. "Overview of the JET preparation for deuterium–tritium operation with the ITER like-wall". Nuclear Fusion (2019): http://dx.doi.org/10.1088/1741-4326/ab2276.
    10.1088/1741-4326/ab2276
  13. "Deep neural networks for plasma tomography with applications to JET and COMPASS". Journal of Instrumentation (2019): http://dx.doi.org/10.1088/1748-0221/14/09/c09011.
    10.1088/1748-0221/14/09/c09011
  14. "Experimental validation of plasma tomography algorithms at ISTTOK". Journal of Instrumentation (2019): http://dx.doi.org/10.1088/1748-0221/14/08/c08006.
    10.1088/1748-0221/14/08/c08006
  15. "Current Research into Applications of Tomography for Fusion Diagnostics". Journal of Fusion Energy (2019): http://dx.doi.org/10.1007/s10894-018-0178-x.
    10.1007/s10894-018-0178-x
  16. "Full-Pulse Tomographic Reconstruction with Deep Neural Networks". Fusion Science and Technology (2018): 1-10. https://doi.org/10.1080%2F15361055.2017.1390386.
    10.1080/15361055.2017.1390386
  17. "Deep learning for plasma tomography using the bolometer system at JET". Fusion Engineering and Design 114 (2017): 18-25. https://doi.org/10.1016%2Fj.fusengdes.2016.11.006.
    10.1016/j.fusengdes.2016.11.006
  18. Diogo R. Ferreira; Pedro J. Carvalho; Horácio Fernandes; JET Contributors. "Robust regression with CUDA and its application to plasma reflectometry". Review of Scientific Instruments 86 11 (2015): 113507-113507. https://doi.org/10.1063%2F1.4935882.
    10.1063/1.4935882
  19. Diogo R. Ferreira; Fernando Szimanski; Célia Ghedini Ralha. "Mining the low-level behaviour of agents in high-level business processes". International Journal of Business Process Integration and Management 6 2 (2013): 146-146. https://doi.org/10.1504%2Fijbpim.2013.054678.
    10.1504/ijbpim.2013.054678
  20. Diogo R. Ferreira; Lucinéia H. Thom. "A semantic approach to the discovery of workflow activity patterns in event logs". International Journal of Business Process Integration and Management 6 1 (2012): 4-4. https://doi.org/10.1504%2Fijbpim.2012.047909.
    10.1504/ijbpim.2012.047909
  21. Michal Walicki; Diogo R. Ferreira. "Sequence partitioning for process mining with unlabeled event logs". Data & Knowledge Engineering 70 10 (2011): 821-841. https://doi.org/10.1016%2Fj.datak.2011.05.003.
    10.1016/j.datak.2011.05.003
Capítulo de livro
  1. "The Tracking Machine Learning Challenge: Accuracy Phase". 2020.
    10.1007/978-3-030-29135-8_9
  2. "A Survey of Process Mining Competitions: The BPI Challenges 2011–2018". 2019.
    10.1007/978-3-030-37453-2_22
Livro
  1. Ferreira, Diogo R.. A Primer on Process Mining. Springer International Publishing. 2020.
    10.1007/978-3-030-41819-9
  2. Diogo R. Ferreira. A Primer on Process Mining. Springer International Publishing. 2017.
    10.1007/978-3-319-56427-2
  3. Ferreira, Diogo R.. Enterprise Systems Integration. Springer Berlin Heidelberg. 2013.
    10.1007/978-3-642-40796-3

Outros

Outra produção
  1. Parallelization of Transition Counting for Process Mining on Multi-core CPUs and GPUs. 2017. Diogo R. Ferreira; Rui M. Santos. https://doi.org/10.1007%2F978-3-319-58457-7_3.
    10.1007/978-3-319-58457-7_3
  2. Improving Business Process Models with Agent-Based Simulation and Process Mining. 2013. Fernando Szimanski; Célia G. Ralha; Gerd Wagner; Diogo R. Ferreira. https://doi.org/10.1007%2F978-3-642-38484-4_10.
    10.1007/978-3-642-38484-4_10
  3. Securely Storing and Executing Business Processes in the Cloud. 2013. David Martinho; Diogo R. Ferreira. https://doi.org/10.1007%2F978-3-642-36285-9_70.
    10.1007/978-3-642-36285-9_70
  4. A Hierarchical Markov Model to Understand the Behaviour of Agents in Business Processes. 2013. Diogo R. Ferreira; Fernando Szimanski; Célia Ghedini Ralha. https://doi.org/10.1007%2F978-3-642-36285-9_16.
    10.1007/978-3-642-36285-9_16
  5. Discovering User Communities in Large Event Logs. 2012. Diogo R. Ferreira; Cláudia Alves. https://doi.org/10.1007%2F978-3-642-28108-2_11.
    10.1007/978-3-642-28108-2_11
  6. Ontology-Based Discovery of Workflow Activity Patterns. 2012. Diogo R. Ferreira; Susana Alves; Lucinéia H. Thom. https://doi.org/10.1007%2F978-3-642-28115-0_30.
    10.1007/978-3-642-28115-0_30
  7. Understanding Spaghetti Models with Sequence Clustering for ProM. 2010. Gabriel M. Veiga; Diogo R. Ferreira. https://doi.org/10.1007%2F978-3-642-12186-9_10.
    10.1007/978-3-642-12186-9_10
  8. Context Inference for Mobile Applications in the UPCASE Project. 2009. André C. Santos; Luís Tarrataca; João M. P. Cardoso; Diogo R. Ferreira; Pedro C. Diniz; Paulo Chainho. https://doi.org/10.1007%2F978-3-642-01802-2_26.
    10.1007/978-3-642-01802-2_26
Atividades

Apresentação oral de trabalho

Título da apresentação Nome do evento
Anfitrião (Local do evento)
2021/10 AI Meets Nuclear Fusion NVAITC Webinar Series on AI Applications in Computational Sciences
Finnish Center for Artificial Intelligence (FCAI) (Helsínquia, Finlândia)
2021/05 Termination of Discharges in High Performance Scenarios in JET 28th IAEA Fusion Energy Conference
International Atomic Energy Agency (IAEA) (Cadarache, França)
2020/12 Deep Learning for Plasma Tomography in Nuclear Fusion NeurIPS 2020 Workshop on Deep Learning and Inverse Problems
(Vancouver, Canadá)
2020/11 Use of HPC Infrastructures for Deep Learning in Fusion Research 1st Spanish Fusion HPC Workshop
(Barcelona, Espanha)
2020/09 Plasma Monitoring and Control with Real-Time Tomography at JET 31st Symposium on Fusion Technology (SOFT2020)
2020/09 Progress in preparing real-time control schemes for Deuterium-Tritium operation in JET 31st Symposium on Fusion Technology (SOFT2020)
2020/07 Identifying Disruption Precursors by Anomaly Detection on Bolometer Tomography IAEA Technical Meeting on Plasma Disruptions and their Mitigation
2019/08 Deep Learning for Plasma Tomography and Disruption Prediction CCFE Physics and Technology Meeting
(Abingdon, Reino Unido)
2019/07 An introduction to Plasma Tomography ISTTOK Training Program on Tokamak Engineering and Operation
(Lisboa, Portugal)
2019/06 Deep Learning for Plasma Tomography and Disruption Prediction MIT-PSFC Machine Learning Working Group
(Boston, Estados Unidos)
2018/10 Applications of Deep Learning to Nuclear Fusion Research NVIDIA GPU Technology Conference
(Munique, Alemanha)
2018/10 Early Identification of Disruption Paths for Prevention and Avoidance 27th IAEA Fusion Energy Conference
International Atomic Energy Agency (IAEA) (Ahmedabad, Índia)
2018/07 An introduction to Plasma Tomography ISTTOK Training Program on Tokamak Engineering and Operation
(Lisboa, Portugal)
2018/07 Regularization extraction for real-time plasma tomography at JET 45th EPS Conference on Plasma Physics
(Praga, República Checa)
2018/06 Analysis of the ramp-down phase of JET ILW discharges 3rd International Conference on Plasma Surface Interactions in Controlled Fusion Devices
(Princeton, Estados Unidos)
2017/06 Full-pulse tomographic reconstruction with deep neural networks 2nd IAEA Technical Meeting on Fusion Data Processing, Validation and Analysis
MIT Plasma Science and Fusion Center (PSFC) (Boston, Estados Unidos)

Arbitragem científica em revista

Nome da revista (ISSN) Editora
2021 - Atual Neural Networks Elsevier
2020 - Atual Fusion Engineering and Design Elsevier
2019 - Atual Plasma Physics and Controlled Fusion IOP
2019 - Atual Nuclear Instruments and Methods in Physics Research Elsevier
2019 - Atual IEEE Transactions on Plasma Science IEEE
2019 - Atual Journal of Instrumentation IOP
2018 - Atual Nuclear Fusion IOP
2017 - Atual Data & Knowledge Engineering (0169-023X) Elsevier
2017 - Atual Review of Scientific Instruments AIP
Distinções

Prémio

2023 Excellence in Teaching Award
Universidade de Lisboa Instituto Superior Técnico, Portugal
2022 Excellence in Teaching Award
Universidade de Lisboa Instituto Superior Técnico, Portugal
2020 Excellence in Teaching Award
Universidade de Lisboa Instituto Superior Técnico, Portugal
2020 IOP trusted reviewer
IOP Publishing Ltd, Reino Unido
2019 Excellence in Teaching Award
Universidade de Lisboa Instituto Superior Técnico, Portugal
2018 Excellence in Teaching Award
Universidade de Lisboa Instituto Superior Técnico, Portugal
2017 Excellence in Teaching Award
Universidade de Lisboa Instituto Superior Técnico, Portugal
2016 Excellence in Teaching Award
Universidade de Lisboa Instituto Superior Técnico, Portugal
2015 Excellence in Teaching Award
Universidade de Lisboa Instituto Superior Técnico, Portugal
2013 Best Paper Award
Universidade do Porto Faculdade de Ciências, Portugal
2013 Excellence in Teaching Award
Universidade de Lisboa Instituto Superior Técnico, Portugal
2012 ICGA Journal Award
International Computer Games Association, Países Baixos
2005 Best Paper Award
Universiteit Twente, Países Baixos