Universit?t Bonn: Autonomous Intelligent Systems Group   Computer Science Institute VI: Autonomous Intelligent Systems

Andreas Mueller

Andreas Müller

Diplommathematiker (Diploma in Mathematics, equiv. M.Sc)
Member of the scientific staff at Autonomous Intelligent Systems Group
Member of the Deep Learning Group


   Andreas Müller
   Rheinische Friedrich-Wilhelms-Universität Bonn
   Institut für Informatik VI
   Friedrich-Ebert-Allee 144
   53113 Bonn 

Tel:  +49 (0) 228 73-4175

Email: amueller _at_ ais.uni-bonn.de

Office: I.10

Short CV

I received my MS degree in Mathematics (Dipl.-Math.) in 2008 from the Department of Mathematics at the University of Bonn. In 2013, I finalized my PhD thesis at the Institute for Computer Science at the University of Bonn. After working as a machine learning scientist at the Amazon Development Center Germany in Berlin for a year, I joined the Center for Data Science at the New York University in the end of 2014. In my position as assistant research engineer at the Center for Data Science, I work on open source tools for machine learning and data science.
I have been one of the core contributors of scikit-learn, a machine learning toolkit widely used in industry and academia, for several years, and I have authored and contributed to a number of open source projects related to machine learning.
You can find my full CV here (pdf).

Research Interests

My research focuses on supervised and unsupervised learning in image data. I am interested in extracting features for modeling image data and for discriminative purposes using structured models. I am also particularly interested in making machine learning and compute vision algorithm available to researchers and practitioners and an easy-to-use way.

Current Work

My current work concerns image segmentation and object detection with weak supervision and latent variables. Whenever I have time I write about my current work and interesting developments in my blog: peekaboo-vision.blogspot.com


PyStruct A structured prediction framework for Python. It implements subgradient and QP based structured SVM solvers, provides a framework for formulating structured problems and has ready-made method for learning loopy CRFs using several inference methods.

scikit-learn Scikit-learn is a general purpose machine learning library in Python. I am release manager and one of the core developers.

scikit-image Scikit-image is a computer vision and image processing library in Python. I contributed some segmentation algorithms.

Python wrappers for GCO I wrote small Python wrappers that make it easier to use the GCO energy minimization software.

CUV library Together with Hannes Schulz and others, I am working on this library, which provides a framework for matrix routines in CUDA and C++, especially designed towards neural networks and Restricted Boltzmann Machines. Using the NVidia Cuda framework it is possible to obtain speedups of 20-100 times compared with a optimized single CPU implementation. There are python bindings available for very fast development. Often an existing Python or Matlab project can easily be converted to the cuv library to obtain huge times speedups without any further optimization.

Restricted Boltzmann Machine and Annealed Importance Sampling Release of our RBM code using CUV for large scale RBM learning with an easy interface. Includes annealed importance sampling and exact calculation of the likelihood for small problems.

Python wrappers for VLfeat I continued work of Mikael Rousson to make the vlfeat library by Andrea Vedaldi and Brian Fulkerson usable with python. In particular I included quickshift wrappers that can be used to create image segmentations and superpixels.

You can find other projects on my Github page.


Andreas Christian Müller:
Methods for Learning Structured Prediction in Semantic Segmentation of Natural Images
PhD Thesis. Published 2014.

Andreas C. Müller and Sven Behnke:
PyStruct - Learning Structured Prediction in Python
Journal of Machine Learning Research (JMLR), 2014.

Andreas Christian Müller and Sven Behnke:
Learning Depth-Sensitive Conditional Random Fields for Semantic Segmentation of RGB-D Images
In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, May 2014.

Andreas Müller and Sven Behnke:
Learning a Loopy Model For Semantic Segmentation Exactly
In Proceedings of 9th International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, January 2014.

Andreas Müller, Sebastian Nowozin and Christoph H. Lampert:
Information Theoretic Clustering using Minimum Spanning Trees
DAGM-OAGM, 2012.

Andreas Müller and Sven Behnke:
Multi-Instance Methods for Partially Supervised Image Segmentation
Accepted for First IAPR Workshop on Partially Supervised Learning (PSL), Ulm 2011.

Hannes Schulz, Andreas Müller, and Sven Behnke:
Exploiting Local Structure in Boltzmann Machines
Neurocomputing 74(9):1411-1417, Elsevier, April 2011.

Hannes Schulz, Andreas Müller, and Sven Behnke:
Investigating Convergence of Restricted Boltzmann Machine Learning
NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning Whistler, Canada, December 2010

Dominik Scherer, Andreas Müller, and Sven Behnke:
Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition
20th International Conference on Artificial Neural Networks (ICANN), Thessaloniki, Greece, September 2010.

Andreas Müller, Hannes Schulz, and Sven Behnke:
Topological Features in Locally Connected RBMs
in the International Joint Conference on Neural Networks (IJCNN 2010)

Hannes Schulz, Andreas Müller, and Sven Behnke:
Exploiting local structure in stacked Boltzmann machines
in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium

Diploma Thesis

I received my Diploma in Mathematics from Rheinische Friedrich-Wilhelms-Universität Bonn.
My Diploma thesis belongs to the field of Arithmetic Algebraic Geometry. It is about classifying a certain kind of singularities in Affine Grassmannians of Linear Algebraic Groups over fields of arbitrary characteristic. Its title is Singularities of Minimal Degenerations in Affine Grassmannians (pdf). Introductions are in both, German and English, but the main part of the thesis is English only.

Universität Bonn, Institute for Computer Science, Departments: I, II, III, IV, V, VI