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PhD in Mathematics of Information

I am a 4th year PhD student at the Cambridge Image Analysis group in the Department of Applied Mathematics and Theoretical Physics. I am also a member of the Cantab Capital Institute for the Mathematics of Information.

My research focuses on generative modeling, with a particular emphasis on diffusion models. Recently, I have become deeply interested in disentangled representation learning and the extraction of data manifold properties from pretrained generative models, including diffusion models and normalizing flows. Additionally, I have been exploring the fascinating area of Schrödinger Bridges.

If any of those topics sound interesting, reach out. I am always happy to chat!


  • Your diffusion model secretly knows the dimension of the data manifold, Jan Stanczuk*, Georgios Batzolis*, Teo Deveney, Carola-Bibiane Schönlieb, arXiv preprint arXiv:2212.12611, 2022 [PDF] (Accepted at ICML 2024)
  • CAFLOW: conditional autoregressive flows, Georgios Batzolis, Marcello Carioni, Christian Etmann, Soroosh Afyouni, Zoe Kourtzi, Carola-Bibiane Schönlieb, arXiv preprint arXiv:2106.02531, 2021 [PDF] (Accepted at Foundations of Data Science, AIMS Journal)
  • How to distribute data across tasks for meta-learning?, Alexandru Cioba, Michael Bromberg, Qian Wang, Ritwik Niyogi, Georgios Batzolis, Jezabel Garcia, Da-shan Shiu, Alberto Bernacchia, Proceedings of the AAAI Conference on Artificial Intelligence, 2022 [PDF]


  • Variational Diffusion Auto-encoder: Latent Space Extraction from Pre-trained Diffusion Models, Georgios Batzolis*, Jan Stanczuk*, Carola-Bibiane Schönlieb, arXiv preprint arXiv:2304.12141, 2023 [PDF]
  • Non-uniform diffusion models, Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, arXiv preprint arXiv:2207.09786, 2022 [PDF]
  • Conditional image generation with score-based diffusion models, Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schönlieb, Christian Etmann, arXiv preprint arXiv:2111.13606, 2021 [PDF]

* denotes equal contribution

Research Group

Mathematics of Information (Applied)




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