Lincen is a Postdoctoral Researcher in the EDA group. Before that, he obtained his PhD in Computer Science in the same group and his Master's in Statistics at the Leiden Mathematical Institute. Lincen's research focuses on interpretable probabilistic models, spanning the development of explainable models for probabilistic classification, conditional density estimation, causal learning, and anomaly detection. His work often reformulates existing, well-known problems, which typically eliminates the need for the ad hoc and heuristic components of current solutions. He is also interested in developing methods without hyperparameters for controlling regularization, which he achieves by designing the learning objective (model selection criterion) under the MDL framework. Lincen has also contributed to applied research in fields such as healthcare (clinical decision making and medical imaging), environmental science, and computer hardware.