Guilherme Aldeia

Guilherme earned his PhD in Computer Science in December 2025, focusing on the computational study and algorithmic improvement of symbolic regression methods and benchmarks. He also holds B.S. degrees in Computer Science and Neuroscience, all from the Federal University of ABC in Brazil.

He is currently a postdoctoral researcher in the Cava Lab, which he joined in February 2026. His current work includes developing methods and applications of symbolic regression and large language models for healthcare, with an emphasis on interpretable decision-making and predictive modeling. He also works on machine learning models using fMRI data and neural network applications for temporal data.

Recent Papers

Towards symbolic regression for interpretable clinical decision scores

Guilherme Seidyo Imai Aldeia, Joseph D. Romano, Fabricio Olivetti de França, Daniel S. Herman, William G. La Cava (2026)

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences

royalsocietypublishing.org  |  pdf  |  code

Iterative Learning of Computable Phenotypes for Treatment Resistant Hypertension using Large Language Models

Guilherme Seidyo Imai Aldeia, Daniel S. Herman, William La Cava (2025)

Machine Learning for Healthcare Conference

proceedings.mlr.press  |  arXiv  |  pdf  |  code

Recent Posts

Simplifying symbolic regression models without simplification rules

6 minute read

Relaxing the definition of equivalent mathematical expressions to get simpler and more interpretable models