Szilard Pafka
Machine Learning Engineer
The Woodlands, Texas, United States
Summary
🤩
RockstarSzilard Pafka is a Machine Learning Engineer with 11 years of experience combining academic rigor from a physics PhD with hands-on data science and benchmarking work. Based in The Woodlands, Texas, he has led and contributed to open-source ML benchmarks that compare scalability, speed, and accuracy across implementations like scikit-learn, XGBoost, H2O and Spark MLlib. He excels at model selection, hyperparameter tuning, and practical evaluation—evidenced by his flight-delay prediction experiments and performance-plotting tools. Beyond engineering, he has experience as a chief (data) scientist, meetup organizer, and visiting professor, blending leadership, community-building, and teaching. Notably, his benchmark-driven approach makes him adept at translating research-grade methods into production-aware, performance-conscious solutions.
11 years of coding experience
Github Skills (8)
xgboost10
machine-learning10
python10
data-science10
r10
h2o9
deep-learning6
deeplearning-ai6
Programming languages (6)
Github contributions (5)
szilard/benchm-ml
Mar 2015 - Aug 2019
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
Role in this project:
Data Scientist
Data ScientistContributions:400 commits, 18 PRs, 498 pushes in 4 years 5 months
Contributions summary:Szilard contributed to the development and evaluation of machine learning models for binary classification within the repository, which focuses on benchmarking machine learning algorithms. They implemented and modified scripts for various algorithms, including random forests, XGBoost, and H2O's implementation of machine learning models, specifically for the task of flight delay prediction. The user also experimented with and integrated various libraries such as scikit-learn and glmnet, demonstrating an understanding of model selection and hyperparameter tuning within the scope of the benchmark. Moreover, the user created and modified scripts to plot performance metrics, suggesting contributions to model evaluation and analysis.
xgboostboosted-treespythongradient-boosting-machinebenchmark
szilard/kaggle-scripts-R-pydata
Jan 2016 - Apr 2017
Contributions:25 commits, 1 PR, 23 pushes in 1 year 2 months
kagglepythondata-sciencekaggle-scriptspython-packages
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