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Professor Michael Fellows is recognized as one of the founders of parameterized complexity, a complexity framework that uses structure in hard problems for the design and analysis of algorithms for their solution. Parameterized complexity has strong connections to algorithmic engineering, and is increasingly important in fields as diverse as Artificial Intelligence, Cognitive Science, and Bioinformatics.
The current challenge Professor Fellows has embraced is to combine this world-leading research with new complexity theory to understand the effectiveness of practical heuristics on real-world datasets, and to systematically design and improve heuristics, based on theory and experiments. He was awarded a Toppforsk grant of about NOK 25 million for his project, “Parameterized Complexity for Practical Computing”, and the grant proposal is available here.
Some of the impactful approaches he is developing include turbocharging heuristics with FPT modules. Fellows' program of reverse kernelization involves incorporating where the data comes from. Together with data visualization, this can significantly contribute to "big data" analysis. It is inevitable that there should be a theory of groovy FPT. The theme here is paying attention to the Structure of Argumentation, such as induction or minimum counter-example. The research answers questions as to how much axiomatic power do you need to prove a particular theorem or to support theorems in computational complexity or multivariate analysis.
The current challenge Professor Fellows has embraced is to combine this world-leading research with new complexity theory to understand the effectiveness of practical heuristics on real-world datasets, and to systematically design and improve heuristics, based on theory and experiments. He was awarded a Toppforsk grant of about NOK 25 million for his project, “Parameterized Complexity for Practical Computing”, and the grant proposal is available here.
Some of the impactful approaches he is developing include turbocharging heuristics with FPT modules. Fellows' program of reverse kernelization involves incorporating where the data comes from. Together with data visualization, this can significantly contribute to "big data" analysis. It is inevitable that there should be a theory of groovy FPT. The theme here is paying attention to the Structure of Argumentation, such as induction or minimum counter-example. The research answers questions as to how much axiomatic power do you need to prove a particular theorem or to support theorems in computational complexity or multivariate analysis.
研究兴趣
论文共 203 篇作者统计合作学者相似作者
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arxiv(2021)
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Treewidth, Kernels, and Algorithmspp.7-17, (2020)
Julien Baste,Michael R. Fellows,Lars Jaffke, Tomás Masarík,Mateus de Oliveira Oliveira,Geevarghese Philip,Frances A. Rosamond
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Adventures Between Lower Bounds and Higher Altitudespp.357-376, (2018)
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Fundamentals of Parameterized Complexity (2016)
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