Teaching

Page Contents

Teaching Philosophy

Data science is a multi-disciplinary field that uses scientific methods and algorithms to extract knowledge and insights from structured and unstructured data.

Recent advances in technology allow for the collection of enormous amounts of health-related data. Consequently, skills pertaining to handling and manipulating these data and extracting relevant information have become crucial to perform high-quality research.

Unfortunately, many researchers without a technical background frequently experience troubles obtaining or developing these skills. This is where I come in.

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Practical Application

I believe in teaching concepts through real-world applications, particularly in healthcare and biomedical research where data science can have immediate impact.

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Accessibility

Complex machine learning concepts should be accessible to researchers from all backgrounds, not just computer scientists.

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Hands-on Learning

Students learn best by doing. My courses emphasize practical exercises and real datasets from biomedical research.

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Critical Thinking

Beyond technical skills, I teach students to critically evaluate methods and results, essential for good scientific practice.

Data Science & ML Courses

Machine Learning for Health Research

Netherlands Institute for Health Sciences (NIHES)

Comprehensive course covering machine learning applications in healthcare research, from basic concepts to advanced techniques.

Supervised Learning Deep Learning Healthcare Applications

Data Science in Epidemiology

Student Materials & Resources

Comprehensive materials for learning data science methods specifically applied to epidemiological research.

Statistical Methods Data Analysis Epidemiology

GEMSTONE Summer School

Genomics of MusculoSkeletal Traits

International summer school focusing on genomics applications in musculoskeletal research using advanced computational methods.

Genomics Computational Methods International

The intersection of domain expertise, programming skills, and statistical knowledge

Interactive Learning Resources

Machine Learning for Health Research - Interactive Slides

Experience machine learning concepts through interactive visualizations and hands-on learning. This comprehensive educational platform makes complex ML algorithms accessible through engaging, interactive content specifically designed for healthcare applications.

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Interactive Navigation

Navigate your ML journey with an interactive mindmap organized by topics and difficulty levels

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Core ML Concepts

Comprehensive coverage of algorithms, optimization, and validation with healthcare examples

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Neural Networks

Interactive visualizations of neural network training, backpropagation, and autoencoders

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Model Evaluation

Visual understanding of bias-variance tradeoff, ROC curves, and performance metrics

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Healthcare Focus

Real-world applications in epidemiology, medical imaging, and clinical decision support

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Knowledge Quiz

Test your understanding with 40 interactive questions covering ML basics

Explore Interactive Slides

Educational Resources & Workshops

Beyond traditional courses, I actively contribute to the broader educational community through specialized workshops, conference presentations, and comprehensive resource collections that support learning in AI, genomics, and computational biology.

Specialized Workshops

LLM in Dementia Research

Workshop

Comprehensive workshop exploring the applications of Large Language Models in dementia research, covering both theoretical foundations and practical implementations.

Large Language Models Dementia Research AI Applications

CHARGE AI Workshop 2024

Conference Workshop

Advanced workshop on AI applications in genomics and population genetics research, featuring cutting-edge methodologies and case studies.

AI in Genomics Population Genetics CHARGE Consortium

Research Resources

XAI for Genomics: Comprehensive Paper Analysis

Publication Resource

Detailed analysis and comprehensive table of Explainable AI methods applied to genomics research, including methodologies, applications, and comparative studies.

Academic Paper Analysis Table

Comprehensive Educational Links

Curated Learning Resources

Resource Collection

Extensive collection of educational resources covering data science, machine learning, genomics, and computational biology. This comprehensive table includes courses, tutorials, papers, and tools organized by topic and difficulty level.

📚 Organized by Topic

🎯 Difficulty Levels

🔗 Direct Links

📖 Resource Types

Nanobiology

I contribute to the Delft Nanobiology program through two complementary teaching activities that prepare students for cutting-edge research at the intersection of biology, physics, and computational science.

Journal Club

To form a truly educated opinion on a scientific subject, you need to become familiar with current research in that field. And to be able to distinguish between good and bad interpretations of research, you have to be willing and able to read the primary research literature for yourself.

Reading and understanding research papers is a skill that every single doctor and scientist has had to learn. But like any skill it takes patience and practice.

In our journal club sessions, students learn to critically analyze recent research papers in computational biology, machine learning applications in biomedical research, and emerging trends in nanobiology. We focus on developing critical thinking skills and the ability to evaluate scientific evidence.

Computational Science

Modern biology generates enormous amounts of data that require sophisticated computational approaches to analyze and interpret. Students learn essential computational skills including data analysis, statistical modeling, machine learning, and bioinformatics tools.

The course covers practical applications in genomics, proteomics, medical imaging, and systems biology. Students work with real datasets and learn to apply computational methods to solve biological problems, preparing them for careers in computational biology and data science.

Program Details