Joeri Hermans


I'm a PhD Student at on quest to mechanize the scientific method by building artificial intelligence systems. This involves working on statistics, likelihood-free inference, machine learning and high performance computing with the goal to effectively apply these to domain sciences such as physics and astronomy. Although I'm not a physicist by training, I'm working towards combining these fields in the future.

Latest Work

Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation

In this work, we introduce Balanced Neural Ratio Estimation (BNRE), a variation of the NRE algorithm designed to produce posterior approximations that tend to be more conservative, hence improving their reliability, while sharing the same Bayes optimal solution.

Averting A Crisis In Simulation-Based Inference

We present extensive empirical evidence showing that current Bayesian simulation-based inference algorithms are inadequate for the falsificationist methodology of scientific inquiry.

Towards constraining warm dark matter with stellar streams through neural simulation-based inference

A statistical analysis of the observed perturbations in the density of stellar streams can in principle set stringent constraints on the mass function of dark matter subhaloes, which in turn can be used to constrain the mass of the dark matter particle. However, the likelihood of a stellar density with respect to the stream and subhaloes parameters involves solving an intractable inverse problem which rests on the integration of all possible forward realizations implicitly defined by the simulation model.