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

Deep Time-to-Event Models for Intrapartum Fetal Monitoring

Guilherme Seidyo Imai Aldeia, Helena Coggan, Yuting Yang, Lisa Levine, Jennifer A. McCoy, William G. La Cava (2026)

Proceedings of Machine Learning Research (PMLR) · Machine Learning for Health (ML4H)

medrxiv.org  |  pdf  |  code  | In Press

Modelling Systemic Effects of Detecting and Reclassifying Under-Triaged Patients in a Pediatric Emergency Department

Helena Coggan, Nathan Georgette, Anne Christine Bischops, Guilherme Seidyo Imai Aldeia, William La Cava (2026)

Proceedings of Machine Learning Research (PMLR) · Machine Learning for Health (ML4H)

pdf  | In Press

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