Machine Learning researcher & Data Scientist β I build models you can actually trust.
- π¬ I work at the intersection of machine learning, explainable AI (XAI), and trustworthy tabular models.
- π I publish peer-reviewed research β Pairwise Difference Learning (Discovery Science 2024), a unified benchmark for XAI methods, and more.
- π¦ I turn research into open-source tools that other people can pip-install and use.
- π¬ Ask me about explainability, benchmarking ML methods, or squeezing more accuracy out of tabular data.
| Project | What it is | |
|---|---|---|
| pdll | scikit-learn compatible meta-learner that boosts tabular classifiers by learning from pairs of points. | β Published @ Discovery Science 2024 |
| Compare-xAI | A unified benchmark to evaluate & compare Explainable AI methods via functional tests. | οΏ½οΏ½οΏ½ Live benchmark |
| the-crawler-search-engine | A from-scratch Java news search engine β BM25, phrase queries, compressed inverted index. No Lucene. | π Systems / IR |
| SGG | Comparing multi-modal fusion functions for Scene Graph Generation (PyTorch). | πΌοΈ Deep learning |
β±οΈ My real-time coding stats
