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Jonathan is a probabilist and mathematical statistician whose research interests lie in random matrix theory, a discipline that originally arose from an interest in estimating the covariance matrix of a random sample obtained from a multivariate population, and which now finds use across several branches of statistics, mathematics, physics, economics and computer science. Recently, his research has focused on applications of random matrix models to high-dimensional data analysis and statistical machine learning, providing him opportunity to explore the growing field of data science. In his free time, he enjoys cycling and swimming, as well as cooking and homebrewing. He also loves playing games (both the tabletop and video varieties), a hobby he enthusiastically shares with his 5-year-old son.