Welcome to the Learning & Adaptive Systems Group at the Department of Computer Science at ETH Zurich. The group is led by Andreas Krause.
Our research is in learning and adaptive systems that actively acquire information, reason and make decisions in large, distributed and uncertain domains, such as sensor networks and the Web. The theoretical aspects include statistical machine learning (online, active, large-scale, ...), probabilistic reasoning and optimization (in particular submodular and non-convex optimization). We devise new algorithms, build models, analyze large and complex data sets and develop systems that can automatically acquire and reason about highly uncertain information. Our application domains include community seismic and traffic sensing, computational sustainability, optimal experimental design and information gathering on the web. See more details here.
- Our paper on A Fresh Perspective: Learning to Sparsify for Detection in Massive Noisy Sensor Networks receives the Best Paper Award at IPSN 2013!
- Andreas Krause received the German Pattern Recognition Award 2012 (Deutscher Mustererkennungspreis; highest German award in pattern recognition, computer vision and machine learning).
- New tutorial on Submodularity in Machine Learning and Computer Vision
- Consider contributing to DISCML 2012: NIPS Workshop on Discrete Optimization in Machine Learning -- Structure and Scalability. See last years videos.
- Andreas Krause receives an ERC Starting Grant 2012.
- Andreas Krause selected as one of the Microsoft Research Faculty Fellows 2012.
- Our paper on Dynamic Resource Allocation in Conservation Planning receives the Outstanding Paper Award at AAAI 2011!
- Matthew Faulkner received the Demetriades - Tsafka – Kokkalis prize in the area of Seismo-Engineering, Prediction, and Protection.
- Download our Matlab toolbox for optimizing submodular functions