Martin Binder


I am a third year PhD scientist at the chaif of Statistical Learning and Data Science at the Ludwig-Maximilian-University Munich, working on automatic machine learning.

At the moment, I am mainly interested in automatic feature preprocessing, feature extraction, and model selection. This encompasses parameterizing the different possibilities of choosing feature preprocessing and modeling pipelines, using black-box optimization methods (for example model based optimization) to choose well-performing parameters for this pipeline, and trading off the cost and accuracy of different performance evaluation methods in a multi fidelity optimization approach.

I am further interested in transfer learning regarding automatic machine learning: Using knowledge gained from previous evaluations of models to reach an acceptable model performance on novel datasets more quickly.

I also have a growing interest in deep learning in the context of biological sequence analysis; possibly also reinforcement learning, general game playing, and theorem proving.

I am the main author of mlrCPO and mlr3pipelines, both R software packages for data preprocessing within machine learning pipelines.

Before finding my way into Computational Statistics and Machine Learning, I obtained an MMath degree in Theoretical Physics and an MSc degree in Biostatistics.


Feel free to write me emails; I don’t care much about formality so you can be be as informal as you like. You can also reach me on the LMU Mattermost Server, I am @mb706 – I greatly prefer Mattermost conversation to email. You can join our Mattermost through this invite link if you haven’t already.

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 148, 1st floor

Phone: +49 89 2180 3521

martin.binder [at]

You Can Find me on


  1. Binder M, Pfisterer F, Bischl B (2020) Collecting Empirical Data About Hyperparameters for Data Driven AutoML AutoML Workshop at ICML 2020,
  2. Binder M, Moosbauer J, Thomas J, Bischl B (2020) Multi-Objective Hyperparameter Tuning and Feature Selection Using Filter Ensembles Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 471–479. Association for Computing Machinery, New York, NY, USA.
  3. Lang M, Binder M, Richter J et al. (2019) mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software 4, 1903.