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Algorithms and bounds for group testing

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If you have a question about this talk, please contact Dr Ramji Venkataramanan.

Group testing was introduced by Dorfman in the 1940s, and gives a model for isolating a small number of infected members of a larger population. I will review recent work on this problem, and explain some new algorithms which can be proved to perform well in certain sparsity regimes. To complement this, I will explain how a channel coding argument of Polyanskiy, Poor and Verdu gives an upper bound on the success rate that can be achieved by any non-adaptive algorithm, by a comparison with a certain statistical hypothesis test. This argument can be modified in the adaptive case, using ideas from directed information theory, corresponding to channel coding with feedback.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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