About

Giorgio Morales is a researcher specializing in Symbolic Regression, Explainable and Interpretable Machine Learning, and Uncertainty Quantification. His work focuses on developing methods that bridge predictive performance and scientific understanding, enabling the discovery of interpretable models with robust uncertainty awareness.

He earned his Ph.D. in Computer Science at Montana State University under the supervision of Dr. John Sheppard, as a member of the Numerical Intelligent Systems Laboratory (NISL). He also holds an M.S. in Computer Science from Montana State University and a B.S. in Mechatronic Engineering from the National University of Engineering in Peru.

His research aims to advance transparent machine learning techniques that support scientific discovery, decision-making under uncertainty, and data-driven exploration across disciplines such as physics, biodiversity, and engineering.