Quentin Stout

Most of his research and teaching involves parallel computing of some form: design of scalable algorithms and data structures; applications to numerous scientific problems such as a large multidisciplinary team modeling space weather or a small interdisciplinary group doing imputation on datasets of social preferences; and performance analysis, both experimental and analytical.  These projects have used a variety of computer architectures, ranging from tens to hundreds of thousands of cores. He also works on algorithms for abstract fine-grain parallel computer models motivated by concerns such as time/number-of-processors/peak-power tradeoffs and the constraints imposed by the fact that computation is done in 2- or 3-dimensional space. Further, he develops serial algorithms for optimizing adaptive sampling problems such as adaptive clinical trials, algorithms for isotonic regression, and various other computer science problems.