Recent research:
This body of work investigates how higher education graduation modalities influence graduate employability, explores bio-inspired optimization for network sampling and addressal of data sparsity in recommender systems, and analyzes cybercrime legislation and its regulatory context. The studies collectively advance understanding of (1) the impact of capstone-based graduation on immediate employability and long-term field alignment, highlighting soft and technical skill development; (2) the efficiency of Ant Colony Optimization for uniform, fast warm-up sampling in large networks like MIT’s Facebook graph, offering a viable alternative to traditional random walks; (3) strategies to mitigate sparsity in collaborative filtering by dimensionality reduction and similarity-based clustering to improve prediction accuracy and learning speed on datasets such as MovieLens 1M; and (4) a comparative examination of Spain and Ecuador’s data protection laws, clarifying structural differences, classification of computer crimes, and regulatory implications for citizens’ personal data. Overall, the research emphasizes practical methods to improve educational outcomes, enhance network analysis techniques, accelerate machine learning in sparse contexts, and inform data protection policy.

Key Topics:

• Education, Pedagogy, Teacher Training & Inclusion
• Research Methods, Measurement, Bibliometrics & Academic Practice
• Artificial Intelligence, Machine Learning & Data Science
• Optimization, Operations Research & Decision Support
• Cybersecurity, Privacy, Trust & Blockchain
• Governance, Law, Policy, Ethics & Regulation