• Since April 2017 - Research Assistant Institut für Informationssysteme at Universität zu Lübeck

• Machine Learning
• Probabilistic Graphical Models
• Lifted Inference
• Temporal and Relational Models

For my PhD, I work on probabilistic first-order formalisms where the domain objects are known. In these formalisms, the standard approach for inference with first-order constructs include lifted variable elimination (LVE) for single queries. To handle multiple queries efficiently and to obtain a compact representation, the lifted junction tree algorithm (LJT) extends LVE. In my thesis, I extend the formalism and respectively LJT to handle temporal aspects. To be more precise, I am interested in solving inference problems, e.g. smoothing, filtering, and prediction, efficiently and to learn relational temporal models from data.