Jeremias Knoblauch is a doctoral research student within the Oxford-Warwick Statistics Programme and based at the University of Warwick, where he is working with Theodoros Damoulas and Chenlei Leng.
His interests revolve around scalable inference methods for spatio-temporal data streams that can run in real time. Inference for complex dynamical systems generating high-dimensional structured data is typically complicated by non-stationarity, changepoints, model uncertainty, misspecification and outliers. While the analysis of real-world data streams almost always needs to address these complications, tackling them jointly leads standard likelihood-based learning rules to break down. Jeremias works on alternative learning rules derived from generalized Bayes theorems which can solve this collection of problems jointly, efficiently and effortlessly. For papers, videos, slides and open-source code, visit his webpage.