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Francisco Baeta is a Ph.D. Student enrolled on the Doctoral Program in Information Science and Technology at the University of Coimbra and member of the Evolutionary and Complex Systems (ECOS) group, holding a Masters's and a Bachelor's degree in Informatics Engineering from the same institution. He is mainly interested in the fields of Generative Models, Evolutionary Computation and Complex Systems, particularly regarding Genetic Algorithms and Generative Adversarial Networks, respectively. His research at both ECOS and the Computational Design and Visualization Lab (both CISUC groups) currently focuses on the application of Evolutionary Algorithms to generative models.
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Identification

Personal identification

Full name
Francisco José Rodrigues Baeta

Citation names

  • Baeta, Francisco

Author identifiers

Ciência ID
3B14-9E35-D03D
ORCID iD
0000-0002-9535-2329
Google Scholar ID
2Tnl5ZYAAAAJ
Education
Degree Classification
2020/12 - 2025/09
Ongoing
 PhD in Informatics Engineering (Doutoramento)
Major in Intelligent Systems
Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Portugal
2018 - 2020
Concluded
 Engenharia Informática (Mestrado)
Major in Sistemas Inteligentes
Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Portugal
 17
2015 - 2018
Concluded
 Engenharia Informática (Licenciatura)
Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Portugal
 16
Projects

Grant

Designation Funders
2021/09/01 - Current Bolsas de Investigação para Doutoramento 2021 - FCT
2021.08254.BD
PhD Student Fellow
Universidade de Coimbra Faculdade de Ciencias e Tecnologia, Portugal
 Fundação para a Ciência e a Tecnologia
Ongoing
2021 - Current GADgET - Online Gambling Addiction Detection
Gadget 718921
Research Fellow
2020 - 2021 Bolsa de Iniciação à investigação EvoGPU
EvoGPU-IMF19. 1/2020.
Scientific Initiation Fellow
Outputs

Publications

Book chapter
  1. Correia, João; Baeta, Francisco; Martins, Tiago. "Evolutionary Generative Models". In Handbook of Evolutionary Machine Learning, 283-329. Springer Nature Singapore, 2023.
    Published • 10.1007/978-981-99-3814-8_10
  2. Machado, Penousal; Baeta, Francisco; Martins, Tiago; Correia, João. "GP-Based Generative Adversarial Models". In Genetic Programming Theory and Practice XIX, 117-140. Springer Nature Singapore, 2023.
    Published • 10.1007/978-981-19-8460-0_6
Conference paper
  1. Baeta, Francisco; Correia, João; Martins, Tiago; Machado, Penousal. "Exploring expression-based generative adversarial networks". Paper presented in GECCO '22: Genetic and Evolutionary Computation Conference, 2022.
    Published • 10.1145/3520304.3534002
  2. Baeta, Francisco; Correia, João; Martins, Tiago; Machado, Penousal. "TGPGAN: towards expression-based generative adversarial networks". Paper presented in GECCO ’22: Genetic and Evolutionary Computation Conference, 2022.
    10.1145/3520304.3529064
  3. Baeta, Francisco; Correia, João; Martins, Tiago; Machado, Penousal. "Speed Benchmarking of Genetic Programming Frameworks". Paper presented in 2021 Genetic and Evolutionary Computation Conference, 2021.
  4. Baeta, Francisco; Correia, João; Martins, Tiago; Machado, Penousal. "TensorGP - Genetic Programming Engine in TensorFlow". Paper presented in 24th International Conference on the Applications of Evolutionary Computations, 2021.
Journal article
  1. Francisco Baeta; João Correia; Tiago Martins; Penousal Machado. "Exploring Genetic Programming in TensorFlow with TensorGP". SN Computer Science 3 2 (2022): https://doi.org/10.1007/s42979-021-01006-8.
    10.1007/s42979-021-01006-8
Thesis / Dissertation
  1. "Genetic Programming in Graphic Processing Units with TensorFlow.". Master, Universidade de Coimbra Faculdade de Ciencias e Tecnologia, 2020. https://estudogeral.sib.uc.pt/handle/10316/93895.
Distinctions

Award

2017 Prémio de 3% dos Melhores Estudantes - Universidade de Coimbra