Prof. Dino Sejdinovic

Assoc. Prof. Dino Sejdinovic is broadly interested in statistical foundations underpinning large-scale machine learning algorithms, with a particular emphasis on nonparametric and kernel methods, and the resulting expressive models. Recent research focused on methods for discovery of higher-order interactions in datasets (when weak individual causes combine in a nonlinear way to form a strong effect), as well as on adaptive simulation suited for target distributions with nonlinear dependencies. Further interests include tradeoffs between statistical, computational and communication efficiency of learning algorithms as well as applications in communications engineering and signal processing.