(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 helsinki.fi
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.
Jukka Corander, University of Helsinki – coordinating professor
Michael Gutmann, University of Edinburgh
Perttu Hämäläinen, Aalto University
Samuel Kaski, Aalto University
Ville Kyrki, Aalto University
Jaakko Lehtinen, Aalto University, NVIDIA
Tuomas Lukka, ZenRobotics
Antti Oulasvirta, Aalto University
Kai Puolamäki, University of Helsinki
Aki Vehtari, Aalto University