Gustavo Daniel Vega Magdaleno

Performed data-driven analysis on aging, caloric restriction, and aging-related diseases using machine learning models (e.g., random forests) in R and Python:

• Developed predictive models using gene ontologies, protein-protein interaction networks, metabolic pathways, and tissue expression data.
• Conducted complex network analysis and gene coexpression studies to identify aging-related gene mechanisms.
• Performed population genomic analysis with PLINK on the UK Biobank database, targeting genes linked to cancer, healthspan, and healthy aging.
• Collaborated on projects involving biological age prediction, drug discovery, stemness analysis, and metabolite association in antiretroviral therapy using Support Vector Machines.

Integrated advanced control and computational techniques to optimize influenza treatment with Oseltamivir, enhancing model predictive control:

• Developed a mathematical model for drug dosage calculation, incorporating viral growth, immune response pharmacodynamics, and pharmacokinetics.
• Designed and trained a recurrent neural network to model drug and infection dynamics, improving data-driven medication estimation and decision-making accuracy through predictive control systems.
• Conducted Monte Carlo analysis to ensure the robustness of the predictive control system.

Served as an assistant lecturer in control theory and analog electronics, delivering both theoretical and practical instruction.