Computer Science | Data Science | Artificial Intelligence
My name is Robin. I am passionate about the intersection of mathematics, algorithms, and programming, with a special focus on Data Science and Artificial Intelligence.
Top rankings in machine learning competitions with proven track record
Master's thesis on VLM capabilities and cognitive perception
Enthusiastic about tackling complex algorithmic challenges
Rijksuniversiteit Leiden
Vrije Universiteit Amsterdam
Investigated how AI vision-language models perceive and associate phonetic sounds with visual shapes, specifically exploring the "bouba-kiki effect" in multimodal artificial intelligence systems.
Quantified sound-shape associations in AI model responses
Evaluated cross-modal understanding capabilities
Integrated phonetic prompts with visual space analysis
In my free time I enjoy participating in Kaggle competitions and tinkering with open datasets!
Applied machine learning techniques to predict passenger transportation outcomes with 4.95% accuracy improvement between versions.
Key Methods: Feature engineering, XGBoost optimization, cross-validation
Impact: Demonstrated systematic approach to model improvement and validation techniques.
Classic machine learning competition focused on survival prediction using passenger data and advanced feature selection.
Key Methods: Data analysis, feature correlation, XGBoost classification
Impact: Achieved significant performance improvement through strategic feature engineering and algorithm selection.
Regression analysis project achieving top 1% ranking through progressive model improvements and advanced feature engineering.
Key Methods: Linear regression, categorical encoding, ensemble methods, data leakage analysis
Impact: Demonstrated understanding of both competitive ML techniques and real-world model applicability.
Weather prediction challenge achieving top 5 ranking with dramatic 28.2% improvement through algorithm optimization.
Key Methods: k-Nearest Neighbors, feature normalization, cross-validation, algorithm comparison
Impact: Proved that simpler algorithms can outperform complex models when properly optimized for specific data patterns.
| Metric | Score | Performance |
|---|---|---|
|
Accuracy
|
99% | |
|
Precision
|
79% | |
|
Recall
|
85% | |
|
F1-score
|
82% | |
|
AUC
|
98% |
Fraud detection system for highly imbalanced financial data achieving 99% accuracy with strong recall performance.
Key Methods: SMOTE sampling, XGBoost classification, model interpretability, performance optimization
Impact: Delivered production-ready solution balancing fraud detection accuracy with explainable AI requirements.
User behavior prediction system for podcast listening patterns using ensemble learning and feature engineering.
Key Methods: Model stacking, feature engineering, time-series validation, ensemble optimization
Impact: Demonstrated advanced ensemble techniques combining multiple algorithms for improved prediction accuracy.
Binary classification project predicting personality type from social behavior and traits using advanced ML techniques.
Key Methods: Feature engineering, SMOTE balancing, stacking ensemble, XGBoost optimization
Impact: Comprehensive analysis of personality prediction with model interpretability and feature importance insights.
Agricultural optimization project for fertilizer recommendation using automated hyperparameter tuning within tight time constraints.
Key Methods: Multi-class classification, automated optimization, ensemble methods, domain-specific features
Impact: Showcased efficient model development and optimization techniques under competitive time pressure.
Systematic Methodology Documentation
🎯 Purpose: End-to-end machine learning process documentation
Reusable ML Components
⚡ Purpose: Optimized functions for streamlined ML workflows