About

Hello! My name is Christoph Molnar and 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 and some more. I regularly publish Mindful Modeler, a newsletter on machine learning.

When I’m not writing or learning, I enjoy cooking and calisthenics.

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

My work experience is a colorful mix of roles as both a classic statistician and in machine learning. I’ve worked across industry, academia, NGOs, and as a self-employed professional. My career has included deep theoretical work, lots of teaching, and extensive hands-on experience with real-world data modeling. Today, my favorite spot is somehwere inbetween: I love bridging from theory to practice. Today, I’m most at home in the space between theory and practice, bridging the two.

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 Occurences

News articles

Podcasts

Keynote Talks

  • Keynote at ECML-PKDD 2020: “Interpretable Machine Learning – State of the Art and Challenges”
  • Keynote at Johner Institutstag 2020: “Interpretable Machine Learning”