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In particular, big data challenges are tackled via randomised, sampling or decomposition approaches that at the same time guarantee some (expected) approximation. | In particular, big data challenges are tackled via randomised, sampling or decomposition approaches that at the same time guarantee some (expected) approximation. | ||
- | Algorithm engineering requires thorough experimentation and evaluation of our new algorithms for real problems. We have experience with a variety of applications such as graph (network) visualisation problems, cheminformatics (drug design), bioinformatics, archeology, statistical physics, and logistic problems such as network design. | + | Algorithm engineering requires thorough experimentation and evaluation of our new algorithms for real problems. We have experience with a variety of applications such as graph (network) visualisation problems, cheminformatics (drug design), bioinformatics, archeology, statistical physics, and logistic problems such as network design and optimization. |
===== Group ===== | ===== Group ===== | ||
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===== Selected Recent Publications ===== | ===== Selected Recent Publications ===== | ||
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+ | * **[[http://www.mdpi.com/2078-2489/9/7/153|More Compact Orthogonal Drawings by Allowing Additional Bends]]** \\ // Michael Jünger, Petra Mutzel and Christine Spisla // \\ Information 2018, 9 (7), MDPI, doi:10.3390/info9010001 | ||
* ** Recognizing Cuneiform Signs Using Graph Based Methods ** \\ // Nils M. Kriege, Matthias Fey, Denis Fisseler, Petra Mutzel, Frank Weichert, // \\ International Workshop on Cost-Sensitive Learning (COST) 2018, Proceedings of Machine Learning Research (PMLR), to appear 2018 (and CoRR abs/1802.05908, 2018) | * ** Recognizing Cuneiform Signs Using Graph Based Methods ** \\ // Nils M. Kriege, Matthias Fey, Denis Fisseler, Petra Mutzel, Frank Weichert, // \\ International Workshop on Cost-Sensitive Learning (COST) 2018, Proceedings of Machine Learning Research (PMLR), to appear 2018 (and CoRR abs/1802.05908, 2018) |