Matthew Aldridge

About

My research involves using ideas from information theory, probability theory, combinatorics, and statistics to analyse search problems and other issues in sparse inference. Using these ideas, I have studied the best known practical algorithms for nonadaptive group testing – providing both theoretical guarantees on performance, and very strong results in simulations – together with rigorous “converse” results, which place tight limits on best-possible performance. Group testing is not only an important problem for its critical applications in medical science, genomics, communications, and beyond, but is also a concrete example of the difficult class of sparse nonlinear inference problems in statistics. I have also applied similar techniques to problems of communication in multiple-user wireless networks.