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Guilherme S. Imai Aldeia

guilherme.seidyo@gmail.com
LinkedIn: https://linkedin.com/in/guilherme-aldeia GitHub: https://github.com/gAldeia Instagram: https://instagram.com/guilhermeseidyo Instagram: https://orcid.org/0000-0002-0102-4958

Guilherme earned his PhD in November 2025 and researches genetic programming and its applications in the health sciences. His works include developing methods for symbolic regression, applying machine learning to fMRI data, and advancing explainable artificial intelligence applications. He is currently interested in evolutionary computing, interpretability in machine learning, optimization methods for symbolic regression, and computational models of neural information processing.

EDUCATION

Federal University of ABC, PhD in Computer Science
Brazil 2025
Universidad de Valparaíso, Latin-American Summer School in Computational Neuroscience
Valparaíso, Chile 2025
Federal University of ABC, B.Sc in Neuroscience
Brazil 2023
Boston Children's Hospital - Harvard Medical School, Visitor PhD Student
Boston, USA 2023 (6 months)
Federal University of ABC, M.Sc in Computer Science
Brazil 2022
Federal University of ABC, B.Sc in Computer Science
Brazil 2020
Federal University of ABC, B.Sc in Science and Technology
Brazil 2019

EXPERIENCE

Teaching Assistant · FIAP
Brazil 2023-2025
  • Assisted in courses on Artificial Intelligence and Machine Learning.
Teaching Internship · Federal University of ABC
Brazil 2020, 2022
  • Developed weekly programming exercises for students (3 months).
  • Teaching assistant for Algorithm Complexity Analysis (3 months).
Researcher
Various Institutions 2019 - ongoing
  • 14 publications with 180+ citations since 2020.
  • Two journal publications (IEEE Trans. Evol. Comput., Genet. Program. Evolvable Mach.) and multiple international conferences (GECCO, ML4HC, WCCI CEC).
  • Peer reviewer for IEEE Trans. Evol. Comput., IEEE WCCI, IEEE CAI, and Royal Society since 2022.
  • 1st Place Undergraduate Final Project at Brazilian Symposium on Information Systems CTCCSI (2020).

SKILLS

Programming Languages
  • Python, C++, Julia
Tools & Systems
  • Linux (10+ years), Bash, SSH, Docker, SLURM, Git, LaTeX, Cloud Computing
Soft Skills
  • Quick learner, scientific outreach, guitar player
Languages
  • Portuguese (native), English (fluent), Spanish (basic)

PROJECTS

Learning Computable Phenotypes for Hypertension, PhD Research Project
Federal University of ABC 2023 - ongoing
  • Using symbolic regression and language models to generate computable phenotypes for hypertension from EHR data.
Understanding Pediatric Headaches with ML and Explainable AI, PhD Research Project
Boston Children's Hospital - Harvard Medical School 2023 - ongoing
  • Training and interpreting ML classifiers on pediatric fMRI data to understand pain mechanisms in youth.
Current challenges of Symbolic Regression, PhD Thesis
Federal University of ABC 2022 - 2025
  • Focused on enhancing model accuracy and interpretability in symbolic regression.
Interpretability in Symbolic Regression, M.Sc Dissertation
Federal University of ABC 2020 - 2022
  • Created new explanation methods and evaluated explanations in symbolic regression.
Functional Connectivity Using Graph Theory to Predict Brain Development, Undergraduate Research
Federal University of ABC 2020
  • Predicted brain functional changes 3 years in the future using graph theory and fMRI data.
Evolutionary Algorithms, Undergraduate Research
Federal University of ABC 2019
  • Studied symbolic regression and proposed the Interaction-Transformation Evolutionary Algorithm (ITEA).

PUBLICATIONS

  1. Aldeia, G. S. I., Romano, J. D., de França, F. O., Herman, D. S., & La Cava, W. G. (2025). Towards symbolic regression for interpretable clinical decision scores.

  2. Aldeia, G. S. I., Herman, D. S., & La Cava, W. G. (2025). Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models. Proceedings of Machine Learning Research, 298, 1–31. https://proceedings.mlr.press/v298/aldeia25a.html

  3. Aldeia, G. S. I., Zhang, H., Bomarito, G., Cranmer, M., Fonseca, A., Burlacu, B., La Cava, W. G., & de França, F. O. (2025). Call for Action: towards the next generation of symbolic regression benchmark. Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO ’25 Companion), 2529–2538. https://doi.org/10.1145/3712255.3734309

  4. Aldeia, G. S. I., Moon, C., Shulman, J., Sethna, N., Smith, A., Lebel, A., La Cava, W. G., & Holmes, S. (2025). Application of Artificial Neural Networks and Functional Brain Connectivity to Inform Pediatric Headache. The Journal of Pain, 29. https://doi.org/10.1016/j.jpain.2025.105140

  5. Aldeia, G. S. I., de França, F. O., & La Cava, W. G. (2024). Inexact Simplification of Symbolic Regression Expressions with Locality-sensitive Hashing. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’24), 896–904. https://doi.org/10.1145/3638529.3654147

  6. Aldeia, G. S. I., de França, F. O., & La Cava, W. G. (2024). Minimum variance threshold for epsilon-lexicase selection. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’24), 905–913. https://doi.org/10.1145/3638529.3654149

  7. Aldeia, G. S. I., & de França, F. O. (2022). Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set. Genetic Programming and Evolvable Machines, 23, 309–349. https://doi.org/10.1007/s10710-022-09435-x

  8. Aldeia, G. S. I., & de França, F. O. (2022). Interaction-transformation evolutionary algorithm with coefficients optimization. Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO ’22), 2274–2281. https://doi.org/10.1145/3520304.3533987

  9. de França, F. O., & Aldeia, G. S. I. (2021). Interaction–Transformation Evolutionary Algorithm for Symbolic Regression. Evolutionary Computation, 29(3), 367–390. https://doi.org/10.1162/evco_a_00285

  10. Aldeia, G. S. I., & de França, F. O. (2020). A Parametric Study of Interaction-Transformation Evolutionary Algorithm for Symbolic Regression. IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC48606.2020.9185521

  11. Spadini, T., Aldeia, G. S. I., & others. (2019). On the application of SEGAN for the attenuation of the ego-noise in the speech sound source localization problem. 2019 Workshop on Communication Networks and Power Systems (WCNPS), 1–4. https://doi.org/10.1109/WCNPS.2019.8896308

  12. Aldeia, G. S. I., & de França, F. O. (2018). Lightweight Symbolic Regression with the Interaction-Transformation Representation. IEEE Congress on Evolutionary Computation (CEC), 1–8. https://doi.org/10.1109/CEC.2018.8477951