About me

I am an assistant professor at the data science group of the Institute for Computing and Information Sciences, Radboud University Nijmegen. I am working on probabilistic machine learning methods and their applications to complex data (such as graphs and time series) in various fields. The application domains include other scientific fields, such as biology, genomics, and econometrics, as well as industrial applications (e.g., energy and energy transition). Some of the ongoing research work also includes machine learning for weather and climate applications.

Previously I worked as a postdoc in the machine learning group of prof. Tom Heskes at the Institute for Computing and Information Sciences, Radboud University Nijmegen. My research during this period focused on probabilistic machine learning for drug-drug interaction and mixture toxicity data. Before joining Radboud University, I worked as a research associate in the group of prof. Magnus Rattray, where I was first exposed to Gaussian processes (GPs), and I continue this line of research up to this day. I received my PhD from Maastricht University with the thesis “Bayesian inference in multivariate nonlinear state-space models”, supervised by dr. Michael Eichler.