GiorgioML

🎸️

Giorgio Morales

PhD in Computer Science

About Me

My name is Giorgio L. Morales Luna. I am a researcher specializing in Symbolic Regression, Explainable and Interpretable Machine Learning, and Uncertainty Quantification. I earned a 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). I hold 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.

My 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. For a complete Academic CV, please visit here.

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Interests

Artificial Intelligence
Explainable Machine Learning
Symbolic Regression
Uncertainty Quantification

Education

Ph.D. in Computer Science
Montana State University
Graduate Certificate in Artificial Intelligence
Montana State University
M.Sc., Computer Science
Montana State University
B.Sc., Mechatronics Engineering
National University of Engineering (Lima, Peru)

📚 My Research

My current research focuses on Symbolic Regression, leveraging the advantages of deep learning techniques (e.g., transformer models) and evolutionary algorithms to distill experimental data into analytical equations that serve as causal explanations for the observable world. The aim is to offer an alternative to the use of black-box models and an avenue for the automated discovery of explanatory and causal models from observed data.

I am also working on uncertainty quantification and adaptive sampling techniques for epistemic uncertainty minimization.

Please reach out to collaborate 😃

Featured Publications

Symbolic Regression

Univariate Skeleton Prediction in Multivariate Systems Using Transformers

Jun 1, 2024

Prediction Intervals

Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation

Jan 1, 2023

Recent Publications

Giorgio Morales, John W. Sheppard (2024). Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural Networks. The 39th Annual AAAI Conference on Artificial Intelligence.

PDF Cite Code Arxiv

Giorgio Morales, John W. Sheppard (2024). Univariate Skeleton Prediction in Multivariate Systems Using Transformers. ECML PKDD 2024. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.

PDF Cite Code Dataset Source Document Springer

Giorgio Morales, John Sheppard (2024). Counterfactual Analysis of Neural Networks Used to Create Fertilizer Management Zones. 2024 International Joint Conference on Neural Networks (IJCNN) (IJCNN 2024).

Cite DOI ARXIV

Giorgio Morales, John Sheppard (2023). Counterfactual Explanations of Neural Network-Generated Response Curves. 2023 International Joint Conference on Neural Networks (IJCNN).

Cite Code DOI

Giorgio Morales, John W. Sheppard (2023). Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation. IEEE Transactions on Neural Networks and Learning Systems.

PDF Cite Code Source Document DOI Arxiv

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