I am a Postdoctoral Researcher in the Department of Psychological Methods at the University of Amsterdam, and I received my PhD in Statistics from Tilburg University. I am also a member of the Bayesian Graphical Modeling Lab, where I collaborate with other researchers on various projects related to Bayesian graphical models and their applications.
My research focuses on developing statistical methods for complex, high-dimensional and network-structured data, addressing problems where standard approaches are not practical. It combines methodology with practical solutions and is organized along three directions:
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Theoretical advances in network and graphical models: seeking novel network and graphical models or extensions of existing frameworks, providing scalable methods, and capturing complex dependencies in high-dimensional data. This line includes model specification, identifiability, hypothesis testing, prior elicitation, and optimal design, such as sample size planning and effective sample size determination.
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Dynamic systems: studying temporal networks and graphical vector autoregressive systems to understand the evolution of dynamic systems. This includes modeling temporal dynamics, real-time evidence update, evaluating interventions affecting network structures or actors.
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Scalable Bayesian methodology: developing Bayesian methods that are computationally feasible and efficient for complex and high-dimensional data. This includes approximation techniques, robust sampling strategies, and code-efficient algorithms.