Research Program 2 (R2)

Simulator-based inference

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 1) and Understandability (objective 3).

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. The other main branch of this 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.

Examples of publications:

Program poster (pdf)

Coordinating professor: Jukka Corander – jukka.corander at helsinki.fi

People

The groups of following professors take part in the Research Program Simulator-based inference. If you would like to join this program, please contact the coordinating professor.