I wrote one of the most widely cited books on machine learning interpretability, used in university courses and industry teams around the world. As a statistician and machine learning expert, I turn academic research into practical resources: books and a newsletter read by thousands of data scientists.
When I’m not writing or learning, I enjoy cooking and calisthenics.
By the Numbers
16k+ Google Scholar Citations
16k+ Newsletter Subscribers
5k+ GitHub Stars
5.5 Books Written
Education
I began my education in classical statistics, but during my Master’s, I developed a strong interest in machine learning. This led me to pursue a PhD in interpretable machine learning.
| Date | Degree | Where | Thesis |
|---|---|---|---|
| Jul 2022 | PhD | LMU | Model-agnostic Interpretable Machine Learning |
| Oct 2014 | MSc in Statistics | LMU | Modeling the booking behavior of customers of a touristic company |
| Sep 2012 | BSc in Statistics | LMU | Automatic Generation of Interactive SVG Maps |
Work Experience
I’ve worked across industry, academia, NGOs, and as a self-employed professional. Today, I’m most at home in the space between theory and practice.
| What | Where | When |
|---|---|---|
| ML Author | Self-Employed | Mar 2022 - now |
| AI Consultant | Johner Institute GmbH | Oct 2021 - Dec 2021 |
| Researcher | BIPS GmbH | Jun 2021 - Sep 2021 |
| PhD Candidate | LMU Munich | Oct 2017 - May 2021 |
| Statistician | SCQM Foundation | Jan 2016 - Aug 2017 |
| Data Scientist | Centralway AG | Nov 2014 - Nov 2015 |
| Side jobs | Various | Oct 2010 - Aug 2014 |
Media
- Maschine, erkläre dich, Süddeutsche Zeitung (German)
- AI Fundamentalists (about Supervised ML for Science)
- AI Fundamentalists
- Machine Learning Street Talk
- Analytics Vidhya
- Johner Institute (German)
- Data Futurology
- Data Skeptic
- Keynote at ECML-PKDD 2020: “Interpretable Machine Learning – State of the Art and Challenges”
- Keynote at Johner Institutstag 2020: “Interpretable Machine Learning”
I write about machine learning with statistical mindfulness — interpretability, uncertainty, and topics beyond predictive performance.
