Past Advising

Students:

• Jaden Brookens - Calvin University, USA (Fall 2024 - Spring 2025)

• Daniel Kwon - Calvin University, USA (Fall 2024 - Spring 2025)

Project Description:

This collaborative senior project developed agent-based models to understand how introversion affects classroom dynamics and learning outcomes, approaching the work from a Reformed perspective that honors the diversity of personalities as part of God’s creative design. Students explored computational approaches to educational psychology, creating simulation frameworks that could inform teaching strategies while respecting the image of God in every learner.

Reformed Faith Integration:

• Imago Dei: Recognizing introversion and extraversion as valid expressions of God’s creative diversity

• Educational Justice: Using research to create more inclusive learning environments for all personality types

• Stewardship of Gifts: Applying computational talents to serve educational excellence and student flourishing

• Community Building: Understanding how different personality types contribute to healthy classroom community

Skills Developed:

• Agent-based modeling techniques grounded in respect for human complexity

• Educational psychology research with Christian anthropological foundations

• Statistical analysis applied ethically and with appropriate humility

• Academic presentation skills that witness to Christian excellence

• Collaborative research that models Christian community values

Outcomes & Kingdom Impact:

• Conference Presentation: “Modeling Introversion in the Classroom: An Agent-Based Approach” at Autonomous Agents for Social Good 2025 - demonstrating how technical work serves human flourishing

• Technical Achievement: Developed reusable simulation framework for educational research with ethical guidelines

• Academic Recognition: Selected for undergraduate research showcase as example of faith-learning integration

• Career Impact: Both students prepared for graduate study and careers that integrate Christian calling with technical expertise

• Ministry Application: Research findings being shared with Christian educators and homeschool communities

Student: Kelly Iapuque Rodrigues de Sousa - Federal University of Lavras, Brazil

Project Description:

Conducted a comprehensive bibliometric analysis using the Consolidated Meta-Analytical Focus Theory to examine the relationship between urban forestry initiatives and public safety outcomes. This interdisciplinary research bridged environmental science, urban planning, and criminology. (Research conducted in Portuguese)

Methodology:

• Systematic literature review and bibliometric analysis

• Meta-analytical statistical techniques

• Environmental policy analysis

• Urban planning framework evaluation

Impact & Publications:

• Published Research: Contributed to urban planning and environmental policy discussions in Brazil

• Policy Relevance: Findings informed municipal forestry planning initiatives

• Academic Recognition: Thesis recognized for methodological innovation

Student: Thiago do Prado Ramos - Federal University of Lavras, Brazil

Project Description:

Analyzed university campus connection logs to develop data-driven approaches for wireless network capacity planning and optimization. The research combined network engineering with big data analytics to solve real-world infrastructure challenges.

Technical Contributions:

• Large-scale log data analysis and pattern recognition

• Network capacity modeling and prediction algorithms

• Performance optimization strategies

• Cost-effective infrastructure scaling solutions

Impact & Applications:

• Practical Implementation: Solutions adopted for campus network improvements

• Industry Relevance: Methodologies applicable to enterprise network planning

• Technical Innovation: Novel approaches to network capacity prediction

Student: Matheus de Andrade Flausino - Federal University of Lavras, Brazil

Project Description:

Combined morphological and topological analysis of cities in Southeast Brazil to understand street robbery patterns using complex network approaches. This research pioneered the application of network science to urban criminology in the Brazilian context.

Research Innovation:

• Complex network modeling of urban topology

• Spatial crime pattern analysis

• Geographic information systems integration

• Predictive modeling for crime prevention

Impact & Recognition:

• Publication: “Exploring the Link Between Urban Topology and Street Crime Using Complex Networks” - Journal of Complex Networks (2025)

• Policy Impact: Results informed urban security planning strategies

• Academic Recognition: Cited by urban planning and criminology researchers

Student: Rolf Pagotto Veiga - Federal University of Lavras, Brazil

Project Description:

Developed innovative machine learning guidelines for automatically identifying and classifying Christian music, combining advanced audio analysis techniques with cultural and theological considerations. This interdisciplinary project bridged technology, musicology, and religious studies.

Technical Achievements:

• Audio feature extraction and analysis algorithms

• Cultural context integration in ML models

• Theological framework consideration in classification

• Scalable music categorization systems

Impact & Innovation:

• Interdisciplinary Contribution: Novel approach to faith-based content analysis

• Technical Innovation: Advanced audio classification methodologies

• Cultural Significance: Preserved and categorized religious musical heritage

Student: Alessandra Louzada Terra - Federal University of Lavras, Brazil

Project Description:

Applied machine learning and graph analysis techniques to study mobility patterns of medical students across Brazil, providing crucial insights for educational policy development and healthcare workforce planning.

Analytical Framework:

• Large-scale educational data mining

• Graph-based mobility pattern analysis

• Predictive modeling for student placement

• Policy impact assessment methodologies

Impact & Policy Relevance:

• Data-Driven Insights: Informed medical education administration decisions

• Policy Implications: Contributed to healthcare workforce distribution planning

• Educational Impact: Enhanced understanding of medical education dynamics in Brazil

Student: Thiago Guedes de Jesus - Federal University of Lavras, Brazil

Project Description:

Developed sophisticated epidemic models using evolutionary game theory dynamics in spatial networks, combining epidemiology with complex systems analysis to understand disease transmission and intervention strategies.

Research Contributions:

• Integration of game theory with epidemiological modeling

• Spatial network analysis for disease spread

• Evolutionary dynamics in public health contexts

• Mathematical modeling of intervention strategies

Impact & Applications:

• Theoretical Advancement: Novel mathematical frameworks for epidemic modeling

• Public Health Relevance: Insights applicable to pandemic response strategies

• Interdisciplinary Innovation: Bridged game theory, epidemiology, and network science

Student: Clayton Ramos da Silva - Federal University of Lavras, Brazil

Project Description:

Applied complex networks and machine learning techniques to model the propagation of Visceral Leishmaniasis, contributing to evidence-based public health intervention strategies in endemic regions of Brazil.

Research Methodology:

• Complex network modeling of disease transmission

• Machine learning prediction algorithms

• Epidemiological data analysis

• Public health intervention optimization

Impact & Publications:

• Publication: “Uso da modelagem baseada em agentes no estudo de sistemas complexos” - Revista Brasileira De Ensino De Física (2025)

• Public Health Impact: Contributed to disease prevention strategies

• Methodological Innovation: Advanced computational approaches to tropical disease research

Student: Leonardo Biazoli - Federal University of Lavras, Brazil

Project Description:

Developed innovative data-driven strategies for extracting insights from large-scale bibliometric datasets, advancing computational methodologies for scientific impact analysis, research trend identification, and scholarly network analysis.

Doctoral Contributions:

• Novel algorithms for bibliometric data processing

• Large-scale scientific network analysis techniques

• Research trend prediction methodologies

• Scholarly impact assessment frameworks

Academic Impact:

• Dissertation Completion: Successfully defended doctoral thesis (2025)

• Methodological Innovation: Advanced scientometrics and bibliometric analysis tools

• Academic Recognition: Contributed to computational social science methodologies

• Future Applications: Methodologies adopted by research evaluation institutions