I’m a statistician, machine learning expert, and writer. I write about machine learning topics that got beyond mere performance: interpretability, uncertainty quantification and the mindsets behind modeling.
I love breaking down arcane knowledge into understandable language. My work is that of a translator and curator of academic results into data science practice. Turning papers into books. So far, I’ve written the books Interpretable Machine Learning and Modeling Mindsets. I regularly publish Mindful Modeler, a newsletter on machine learning.
When I’m not writing or learning, I enjoy cooking and calisthenics.
My Books
- Introduction To Conformal Prediction With Python: A Short Guide For Quantifying The Uncertainty Of Machine Learning Models
- Interpretable Machine Learning: Making Black Box Models Explainable
- Supervised Machine Learning for Science: How to stop worrying and love your black box
- Modeling Mindsets: The Many Cultures Of Learning From Data
- Interpreting Machine Learning Models With SHAP: A Guide With Python Examples And Theory On Shapley Values
My Newsletter
Media Occurences
News articles
Podcasts
- AI Fundamentalists
- Machine Learning Street Talk
- Analytics Vidhya
- Johner Institute (German)
- Data Futurology
- Data Skeptic
Keynote Talks
- Keynote at ECML-PKDD 2020: “Interpretable Machine Learning – State of the Art and Challenges”
- Keynote at Johner Institutstag 2020: “Interpretable Machine Learning”