Computer Science
Andreas C. Müller develops open source software for machine learning and data analysis. He is a core-developer of the scikit-learn project and is co-author of the book “Introduction to Machine Learning with Python”.
Müller works on providing easy-to-use tools for scientists to apply machine learning methods, and provide educational material around machine learning, with the goal of lowering the barrier of entry to data driven research.
- Assistant research scientist at NYU Center for Data Science, 2014-2016
- Lecturer in data science, Columbia University, 2016-
- Machine learning scientist, Amazon Development Center, Germany, 2013-2014
- Müller, A and Guido, S. (2016) Introduction to Machine Learning with Python, O’Reilly.
- Varoquaux, G., L. Buitinck, G. Louppe, O. Grisel, F. Pedregosa, and A. Müller (2015). Scikit-learn: Machine Learning Without Learning the Machinery. GetMobile: Mobile Computing and Communications
- Abraham, A., F. Pedregosa, M. Eickenberg, P. Gervais, A. Müller, J. Kossaifi, A. Gramfort, B. Thirion, and G. Varoquaux (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics.
- Müller, A. and S. Behnke (2014). PyStruct: Structured Prediction in Python, Journal of Machine Learning Research.
- Müller, A. and S. Behnke (2014). Learning Depth-Sensitive Conditional Random Fields for Semantic Segmentation of RGB-D Images. In: Proceedings of the International Conference of Robotics and Automation (ICRA).
- Müller, A., S. Nowozin, and C. Lampert (2012). Information Theoretic Clustering Using Minimum Spanning Trees. In: Proceedings of DAGM / OAGM, pp.205–215.
- Scherer, D., A. Müller, and S. Behnke (2010). Evaluation of pooling operations in convolutional architectures for object recognition. In: Proceedings of the Interntional Conference on Artificial Neural Networks (ICANN). Springer, pp.92–101.