Jeremias is doctoral researcher working at the nexus of computer science and statistics within the Oxford-Warwick Statistics Programme (OxWaSP). He is jointly supervised by Theodoros Damoulas and Chenlei Leng. He is also a Visiting Researcher at The Alan Turing Institue in London, where is affiliated with the London Air Quality project to support London’s Major’s office with data-driven policy.
Jeremias' interests focus on scalable spatio-temporal inference procedures for data generating mechanisms in high dimensions that are ill-behaved or difficult to describe. In particular, he develops the next generation of on-line methods for complex dynamical systems that are affected by a multitude of phenomena such as non-stationarity, change points, model uncertainty, misspecification and outliers all at once. While the analysis of real-world data streams almost always needs to address these complications, tackling them jointly leads standard inference rules to break down. In contrast, his research shows that sophisticated learning rules derived from Generalized Bayesian Updating solve these problems efficiently and effortlessly.