Title: Diffusion Models for the Next Generation of Simulation-Based Inference: Challenges and Opportunities

Abstract: Diffusion models have recently emerged as powerful tools for simulation-based inference (SBI). SBI tackles the problem of estimating complex models from complex data, and the score-based formulation of diffusion models offers unmatched flexibility for inference and generation. This talk will synthesize recent advancements in diffusion models for SBI, focusing on key developments in training, inference, and evaluation. It will explore the potential of concepts such as guidance, score composition, flow matching, consistency models, and joint modeling. Finally, it will showcase three recent contributions from our group: 1) consistency models for near-real-time Bayesian inference; 2) compositional models for large-scale multilevel estimation, and 3) amortized Bayesian adaptive design for maximizing information gain in sequential experiments.

Bio: Stefan T. Radev is the principal investigator of the BayesOps Lab. He leads research on amortized Bayesian inference with generative AI and is a core developer and originator of the BayesFlow framework. He is particularly interested in the intersection of Bayesian modeling, deep learning, and cognitive science. His education and experience include:

2018 – 2021 – Ph.D., Heidelberg University (Statistical Modeling in Psychology)
2021 – 2023 – Postdoctoral Researcher, Heidelberg University (Deep Learning)
2023 – present – Assistant Professor, Rensselaer Polytechnic Institute