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Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The mathematical study of fundamental objects such as curves, embedded graphs, surfaces and 3-manifolds has a rich and old history. Research into their algorithmic and combinatorial properties, and underlying computational questions, on the other hand, is still young. From the complexity-theoretic side important open problems are hardness of realizability, fine-grained complexity of distance and similarity measure computations, existence of polynomial-time algorithms for flip distances, or approximability of such distances. Combinatorics and algebra come into play when dealing with associated polyhedral structures such as associahedra and secondary polytopes, and mapping class groups of surfaces. Moreover, applied fields such as trajectory analysis and machine learning yield new questions and perspectives.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The past decade has seen a rapid growth in the ability to collect large-scale spatiotemporal data sets about sports. Ideally, such data should be used to inform strategic and tactical decision making. On the one hand, strategy is the long-term planning of training sessions, signing of coaches and athletes, rotation and the plan made before a match/race. On the other hand, tactics are short-term and involve the execution and adaptation to the match/race plan. Having insights into the efficacy and feasibility of strategies and tactics is particularly important and challenging within sports because effective and novel strategy & tactics allow weaker teams or athletes to win against stronger ones. Unfortunately, the size, richness, and complexity of modern spatiotemporal sports data means that automated analysis is essential. Alas, the nature of the data has posed a number of challenges for classic analysis techniques. This has spurred the development of novel statistical and machine learning techniques in order to perform more fine-grained analysis of every action and decision during a competitive event. In this Dagstuhl Seminar, we aim to bring together sports researchers in academia and industry to understand how they are using machine learning and statistical techniques to analyze strategy and tactics.

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Optics and Lasers,    Computational Physics and Numerical Simulation

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)