Ludvig Renbo Olsen

MSc Student in Cognitive Science

Aarhus University


Ludvig R. Olsen is currently a master’s student in Cognitive Science at Aarhus University. He has developed three R packages, groupdata2, cvms and xpectr, and written two chapters (4 & 5) for Practical Machine Learning with R. He is active in Effective Altruism Aarhus and intends to use Effective Altruism (EA) to guide his career choices.


  • Effective Altruism
  • Replacing Animals in the Food System
  • Machine Learning and AI
  • Building tools and tutorials
  • Audio Production


  • BSc in Cognitive Science, 2018

    Aarhus University



4-5 years of experience

Machine Learning

TensorFlow, scikit-learn, tidymodels


4-5 years of experience


Frequentist and Bayesian

Natural Language Processing

Data cleaning, transfer learning, classification

Audio Production

Logic, Ableton Live, recording, mixing



Machine Learning Researcher


Jun 2017 – Jul 2018 Aarhus
I worked ~25h a week on Natural Language Processing challenges, mostly using TensorFlow and python.

  • Data cleaning
  • Sentence classification
  • Extending codebase

Recent Posts

cvms 1.0.0 released

After 6 months of work, cvms version 1.0.0 has finally been released! This version is a major refactoring of the package and includes …

New website

I have changed my website to the Hugo platform and the academic theme. This allows me to create my blog posts and tutorials in …

groupdata2 version 1.1.0 released on CRAN

A few days ago, I released a new version of my R package, groupdata2, on CRAN. groupdata2 contains a set of functions for grouping …

cvms 0.1.0 released on CRAN

After a fairly long life on GitHub, my R package, cvms, for cross-validating linear and logistic regression, is finally on CRAN! With a …

Running cross_validate from cvms in parallel

The cvms package is useful for cross-validating a list of linear and logistic regression model formulas in R. To speed up the process, …

Recent Publications

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Practical Machine Learning with R

Chapters 4 and 5 for Practical Machine Learning with R, where I cover regression and classification with linear models and simple …