Recent advances in image and video capture have enabled very cheap acquisition of image data. This calls for automatic tools for analysis, categorization and interpretation. Such challenges in the field of computational vision present an ideal test bed for pattern recognition and machine learning algorithms. We apply new sampling techniques to image segmentation, investigating the different approaches for efficient large scale inference. Graphical models are used to learn object categorization from labeled data, providing a framework for integrating multiple sources of information.

Members:

 - Thomas Fuchs
 - Verena Kaynig
 - Patrick Pletscher
 - Alexander Vezhnevets

With the advent of high throughput methods for genomics and proteomics, automated tools for processing and analysis of the large amounts of data is highly necessary. Our efforts are focused on using machine learning and statistical techniques, to solve complex problems arising in biological and medical context. We emphasize on applications of kernel-based analysis, network inference and probabilistic modeling. The research is inspired by real-world problems and data, that arise from our established collaborations with biologists and medical scientists. In collaboration with other groups, we are further interested in using the predictions of our machine learning algorithms to guide biological experiments.

Members:

 - Manfred Claassen
 - Xenofon Floros
 - Alberto Giovanni Busetto
 - Thomas Fuchs
 - Verena Kaynig
 - Cheng Soon Ong

The human auditory system selects relevant sounds from noise and irrelevant acoustic input. For hearing impaired persons, this ability is often significantly reduced. Furthermore, resolution in time and frequency is degraded, which makes it difficult to accurately locate a source. In collaboration with Phonak AG, we develop methods to analyze acoustic scenes and hearing instrument wearers' needs, with the goal of optimal adaptive control of the hearing instrument. Our current research focuses on hierarchical classification, component analysis, unsupervised and semi-supervised online learning, and model based signal processing.

Members:

 - Christian Sigg
 - Andreas Streich
 - Yvonne Moh

We study the statistical and algorithmic principles behind learning from data. One key issue is the notion of complexity, and how to control it while finding a model that is in agreement with the observed measurements. We focus our efforts on structured data, investigating approaches such as clustering, graphical models and dynamical systems. A major effort of our algorithmic and modeling work is devoted to quantify the robustness of the learned structures, i.e., to provide the data analyst with uncertainty estimates of models and methods.

Members:

 - Mario Frank
 - Ludwig Busse
 - Alberto Giovanni Busetto
 - Yvonne Moh
 - Patrick Pletscher
 - Cheng Soon Ong