Simulator-based inference

(The FCAI research programs are currently in a ramp-up phase. More information will be updated here later.)

The goal of FCAI’s research program Simulator-based inference is to develop methodology for the new AI having efficient, interpretable reasoning capability, by cross-breeding modern machine learning and simulator-based inference. Simulator-based inference contributes to mainly FCAI research objectives Data efficiency (objective I) and Understandability (objective III).

Current research in Simulator-based inference includes Engine for Likelihood-free Inference (ELFI) software, which builds a community-driven ecosystem of simulator models and inference algorithms. The new method has accelerated inference by several orders of magnitude. Another branch of the research program includes groundbreaking work on simulator-based deep learning and generative adversarial networks (GANs).

The advances in high-dimensional models and causal AI-based reasoning will make inference in human-AI interaction possible using cognitive models, in areas such as personalized medicine (e.g., cancer therapy assistance to clinicians), widely deployed R&D tools for chemists, engineers, and physicists (e.g., for materials design), and modeling for multiple other applications by enabling significantly more complex models based even on limited data.

Coordinating professor: Jukka Corander – jukka.corander at


The groups of following professors already take part in the research program Simulator-based inference. The list is currently under construction. If your group is already involved and needs listing here, please contact the program coordinator.