About me

I am an assistant professor in the data science group at the Institute for Computing and Information Sciences, Radboud University Nijmegen.

My research focuses on building machine learning systems that can reason under uncertainty and learn from multiple sources of information. I am particularly interested in probabilistic and generative approaches for complex data, including time series, graphs, and spatiotemporal fields such as radar and satellite image sequences. My work draws on Gaussian processes, probabilistic deep learning, state-space models, and deep generative models, with a methodological focus on uncertainty quantification and multimodal learning.

Beyond methodology, I enjoy being challenged by real-world applications and working on systems that move beyond the research lab into operational use. My research spans applications in weather and climate, energy systems and the energy transition, among other domains where decisions must be made under uncertainty. I am particularly motivated by building models that are not only scientifically rigorous but also deployed in practice and capable of supporting real-world decision making.

Previously I was a postdoc with prof. Tom Heskes at iCIS, and I hold a PhD from Maastricht University (“Bayesian Inference in Multivariate Nonlinear State-Space Models”), supervised by dr. Michael Eichler. My work on Gaussian processes began in prof. Magnus Rattray’s group in Manchester.