CV

Contact

fabricio.olivetti @ gmail.com

folivetti @ ufabc.edu.br

link to CV in PDF

Objectives

To contribute to the creation of Symbolic Regression algorithms and supporting tools enabling the discovery of general scientific laws and interpretable nonlinear regression model. To advance in the field of explainable and interpretable models, in scenarios where transparency is required. To push the limits of the interpretability, simplicity, and accuracy trade-off in fields that require accurate and transparent decision process.

Education

• B.S. in Electrical and Computer Engineering, Universidade Católica de Santos (UniSantos), 2003.
• M.S. in Electrical and Computer Engineering, Universidade Estadual de Campinas (Unicamp), 2005.
• Ph.D in Electrical and Computer Engineering, Universidade Estadual de Campinas (Unicamp), 2010.
• Visiting Professor at University of Applied Sciences Upper Austria, FH, Austria, 2022.

Work experience

• 2018-current: Head of the Heuristics, Analysis and Learning Laboratory (HAL)
  • Objectives: advance in the research of heuristics and machine learning algorithms, support the educational background of the university students, create opportunities for collaboration with other academic institutes and the industry.
• 2023-2026: Coordinator of the graduate program of Computer Science at the Universidade Federal do ABC (UFABC)
  • Objectives: creating and executing plans to increase the research impact of the members of the prgram
• 2012-current: Professor of Computer Science at the Universidade Federal do ABC (UFABC)
  • Roles: supervising bachelor, Master’s, and Ph.d.’s students, teaching undergrad and graduation level courses, researching and coordinating research teams.
• 2010-2012: Research Consultant at Tuilux
  • Roles: Combinatorial Optimization applied to Logistics, Recommender Systems, Sentiment Mining, Consumer Behavior and Big Data

Research interests

• Machine Learning, Data Mining, Data Science and Big Data
  • Symbolic Regression
  • Genetic Programming
  • Interpretability and Explainability
  • Program Synthesis
  • Equality Saturation and e-graphs
  • Efficient Data Structures
  • Domain knowledge integration
• Optimization Problems
  • Multimodal Optimization
  • Uncertainties
  • Meta-heuristics

Past and Current Projects

• Shape-constrained Symbolic Regression (2023): Integrated multi-objective optimization for robust extrapolation in machine learning models. FAPESP 21/12706-1.
• Interaction-Transformation Symbolic Regression (2020): Applied innovative algebraic data types for enhanced symbolic regression tasks. FAPESP 18/14173-8.
• Improving the Search of Symbolic Regression (2024-2026). CNPq 301596/2022-0.
• Internet Conflicts Observatory (2020). FAPESP 18/23022-3.
• Data Science. CAPES Print (2018-2025)
• The role of social media in comparative elections: Turkey and Brazil (2016). FAPESP 15/50250-9.
• Electoral Disputes in Cyberspace: study of social networks in Brazilian presidential elections (2014). FAPESP 14/06331-1.

Publications

• Google Scholar profile
• Lattes CV
• 10 highlighted publications:
• La Cava, W., Burlacu, B., Virgolin, M., Kommenda, M., Orzechowski, P., de França, F. O., … & Moore, J. H. (2021). Contemporary symbolic regression methods and their relative performance. Advances in neural information processing systems, 2021(DB1), 1. arxiv
• Kronberger, G., de França, F. O., Burlacu, B., Haider, C., & Kommenda, M. (2022). Shape-constrained symbolic regression—improving extrapolation with prior knowledge. Evolutionary Computation, 30(1), 75-98. arxiv
• de Franca, F. O., Virgolin, M., Kommenda, M., Majumder, M. S., Cranmer, M., Espada, G., … & La Cavaz, W. G. (2024). SRBench++: Principled benchmarking of symbolic regression with domain-expert interpretation. IEEE transactions on evolutionary computation. pub
• Aldeia, G. S. I., & de Franca, F. O. (2022). Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set. Genetic Programming and Evolvable Machines, 23(3), 309-349. arxiv
• Russeil, E., de França, F. O., Malanchev, K., Burlacu, B., Ishida, E., Leroux, M., … & Gangler, E. (2024, July). Multiview Symbolic Regression. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 961-970). arxiv
• Fernandes, M.C., França, F.O.d., Francesquini, E. (2025). Going Bananas! - Unfolding Program Synthesis with Origami. In: Paes, A., Verri, F.A.N. (eds) Intelligent Systems. BRACIS 2024. Lecture Notes in Computer Science(), vol 15413. Springer, Cham. https://doi.org/10.1007/978-3-031-79032-4_1 arxiv
• de França, F. O., & Kronberger, G. (2025). Improving Genetic Programming for Symbolic Regression with Equality Graphs. arXiv preprint arXiv:2501.17848. arxiv
• de Franca, F. O., & de Lima, M. Z. (2021). Interaction-transformation symbolic regression with extreme learning machine. Neurocomputing, 423, 609-619. pub
• de França, F. O. (2022, July). Transformation-interaction-rational representation for symbolic regression. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 920-928). arxiv
• de Franca, F. O., & Kronberger, G. (2023, July). Reducing Overparameterization of Symbolic Regression Models with Equality Saturation. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 1064-1072). pub

Teaching Experience

• Symbolic Regression
• Introduction to Programming
• Type Driven Development
• Functional Programming
• Artificial Intelligence videos
• Network Science
• Bio-Inspired Computing
• Explainable AI
• Category Theory for Programmers
• Machine Learning with Big Data
• Data Structures
• Machine Learning
• Structured Programming
• Formal Languages and Automata
• Information Theory
• Video lectures for some of these courses (in Portuguese): HAL YT Channel

Technical Skills

• Main programming languages: Python, R, MATLAB, C++, Haskell
• Frameworks: Scikit-Learn, TensorFlow
• Algorithms: Symbolic Regression, Genetic Programming, Equality Saturation, Optimization, Data Structures

Achievements

• Lectures on 14 different courses including Machine Learning and Evolutionary Computing at grad- uate levels.
• Supervised over 20 undergraduate, Master’s and Ph.D. students in Computational Intelligence.
• Creator and maintainer of SRTree library for tree-based symbolic regression.
• Developer and co-creator of eggp algorithm for symbolic regression.
• Developer and co-creator of r🥚ression a nonlinear regression models exploration and query system withe-graphs
• Active contributor and current co-organizer of SRBench
• Organizer of the Genetic Programming Theory & Practice XXII
• Organizer of two Symbolic Regression competitions in 2022 and 2023
• Organizer of EvoMan competition in 2019
• Part of the editorial board of Genetic Programming and Evolvable Machines.
• Active referee for journals such as Neurocomputing, IEEE TEVC, information Sciences, Scientific Reports, GPEM, GECCO, CEC, NeurIPS.

Programming languages that I have written at least one line of code :-) (in alphabetic order, main languages in bold):

• Assembly
• ActionScript
• AWK
• Bash
• BASIC
• Brainf*ck
• C
• C++
• C#
• D
• Delphi
• Fortran
• Go
• Haskell
• Java
• Javascript
• Julia
• Kotlin
• Lisp
• Logo
• Matlab
• NetLogo
• OCaml
• Octave
• Pascal
• PureScript
• PHP
• Prolog
• Python
• R
• Scala
• Scilab
• Sed
• SQL
• TASM
• TeX
• TypeScript
• Unity
• Visual Basic
• Wolfram language
• Z shell