I am a second year PhD scientist at the working group for Computational Statistics 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.
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
Room 148, 1st floor
Phone: +49 89 2180 3521
martin.binder [at] stat.uni-muenchen.de
You Can Find me on
- Binder M, Moosbauer J, Thomas J, Bischl B (2020) Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles. arXiv preprint arXiv:1912.12912.
- 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.