We feel that algorithms research benefits from a certain breadth of topics, and we have been aiming to cover that breadth in a particular way that we want to continue to explore. In terms of computer science methods, our interest ranges from hard combinatorial and geometric optimization problems across data structures for large data sets and complex operations to distributed data handling and processing. In terms of application scenarios, we plan to continue the study of transportation problems such as railway optimization and control; of mobile phone antenna optimization problems such as placement, frequency assignment, and adaptivity; of proteomics problems such as database search and data analysis problems; of web related problems such as fault tolerant data handling and processing in a large network. All of these problems will be studied under a variety of conditions and objectives. In terms of mode of operation, we feel that it is important to be part of a research effort already in the modelling phase, carry all the way through the abstraction and solution phase to the phase of implementing and experimenting with algorithms and data structures. We are aware of the danger inherent in such an approach: Too much breadth certainly comes with a lack of depth, and too little breadth leads to a lack of cross-fertilizing experience. Our goal is to balance both in a way that allows us to do fundamental research and application development at the same time, and not disregard the area in between.