ProfessorPeter Radchenko

Peter Radchenko's research focusses on developing and analyzing novel methodology for dealing with massive and complex modern data. Fields ranging from finance, marketing and economics to image analysis, signal processing, data compression and computational biology nowadays share the common feature of trying to extract information from vast noisy data sets. The age of Big Data has created an abundance of interesting problems, posing new challenges, not present in conventional data analysis. Such large scale problems fall under the general framework of High Dimensional Statistics and Statistical Machine Learning, which are the primary areas of Peter Radchenko's research. His contributions to the field cover a wide range of diverse topics including high-dimensional regression, large-scale clustering, and functional data analysis.

Peter Radchenko's work on high dimensional regression problems involves fitting models and performing variable selection in settings where the number of predictors is large relative to the number of observations. His extensive methodological and theoretical research in this area covers a wide range of topics, including linear and nonlinear additive models, nonlinear interaction models, generalized linear models, and single index models.

A recent new direction of Peter Radchenko's research takes advantage of the impressive advances in mixed integer optimization and modern optimization techniques to solve and analyse critically important discrete problems arising in statistics. Together with his collaborators, he has developed novel mixed integer optimization-based approaches for fitting sparse high-dimensional linear and nonlinear additive models. He also made significant contributions to the analysis of the best subset selection approach in low-signal high-dimensional regimes.