Machine Learning in Julia

MLJ (Machine Learning in Julia) is a toolbox written in Julia that provides a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing machine model implementations written in Julia and other languages. More broadly, the MLJ project hopes to bring cohesion and focus to a number of emerging and existing, but previously disconnected, machine learning algorithms and tools of high quality, written in Julia. A welcome corollary of this activity will be increased cohesion and synergy within the talent-rich communities developing these tools. In addition to other novelties outlined below, MLJ aims to provide first-in-class model composition capabilities. Guiding goals of the MLJ project have been usability, interoperability, extensibility, code transparency, and reproducibility. Paper

Associate Professor in Mathematics & Statistics

My research interests lie at the interface of applied probability, statistical inference and machine learning.