Rate My Professor Oliver Johnson

Johnson's research centers on applying information theory to comprehend limit theorems in probability and statistics, with a particular emphasis on discrete random variables. His work encompasses transportation of measure, proofs of the Shepp-Olkin entropy concavity and monotonicity conjectures, log-Sobolev inequalities, maximum entropy distributions, monotonicity properties, and group testing, including practical algorithms and fundamental converse bounds. He has authored key books such as 'Information Theory and the Central Limit Theorem' (2004, World Scientific) and 'Numbercrunch,' aimed at a general audience (published 2023). Notable publications include 'Group testing: an information theory perspective' (2019, Foundations and Trends in Communications and Information Theory), 'Group testing algorithms: Bounds and simulations' (2014, IEEE Transactions on Information Theory), 'Fisher information inequalities and the central limit theorem' (2004, Probability Theory and Related Fields), and 'The capacity of adaptive group testing' (2013, IEEE International Symposium on Information Theory). Johnson engages in collaborative interdisciplinary efforts with engineers and computer scientists to explore randomness through theoretical calculations. He is a Fellow of the Academy for the Mathematical Sciences and contributes to public understanding of mathematics through writing and lectures.