Jonas Arruda

Jonas Arruda, a researcher who explains diffusion models and generative modeling, gives a clear mini-tutorial. He walks through starting from Gaussian noise and iteratively denoising to produce samples. He contrasts the forward noising process with the learned backward denoising. He also outlines training with noisy parameters and the role of noise schedules like alpha and sigma.

Jonas Arruda, mathematician and PhD researcher at the University of Bonn and core contributor to Baseflow. He explores using diffusion models for simulation-based inference. Short demos show building simulators, training amortized posteriors, and sampling multimodal results. Discussion covers score learning, reverse sampling, flow-matching alternatives, diagnostics like coverage/SBC, and practical tips for scaling and guided inference.