Dmitry Vetrov

Prof. Dmitry Vetrov is a Professor of Computer Science at Constructor University Bremen, affiliated with the School of Computer Science and Engineering. His research focuses on integrating Bayesian methodologies with deep learning, particularly in diffusion models, generative adversarial networks (GANs), and domain adaptation. He leads the Bayesian Method Research Group and has contributed to advancements in areas such as neural optimal transport, unsupervised voice restoration, and genetic fine-mapping.

Key research interests include probabilistic modeling, generative AI, and optimization techniques for neural networks. Notable work includes innovations in diffusion samplers, adaptive learning rate analysis, and encoder-based approaches for image and audio generation. His publications span top venues like NeurIPS, ICLR, and AAAI.

Current projects emphasize scalable diffusion models, thermodynamic views of SGD, and Bayesian approaches in genetic analysis. He collaborates on applied areas like speech enhancement (HIFI++), protein sequence generation, and efficient parameterization of GANs.

Labs/Teams: Leads the Bayesian Method Research Group, actively involved in Constructor University's AI and machine learning initiatives.