Generative AI group at TU/e

The Generative AI group focuses on building deep generative models (a combination of probabilistic modeling and deep learning) that could be used for defining generative processes, synthesizing new data, and quantifying uncertainty. The research carried out within the Generative AI group is reinforced by multiple applications in Life Sciences (biology, biochemistry), Molecular Sciences (chemistry, physics), and problems ranging from signal processing (e.g., data compression) to self-driving cars, and smart devices, and smart apps (e.g., chatbots, art generation).

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Jakub M. Tomczak

Associate professor
(Group PI)

Babak Esmaeili


Haotian Chen

Ph.D. candidate

Mahdi Mehmanchi

Ph.D. candidate

Anna Kuzina

Ph.D. candidate
(External at Vrije Univ. Amsterdam)

Sharvaree Vadgama

Ph.D. candidate
(External at the Univ. of Amsterdam)

Adam Izdebski

Ph.D. candidate
(External at the Helmoltz Munich)

Jan Engelmann

Ph.D. candidate
(External at the Helmoltz Munich)

In our research, we focus on developing new probabilistic models parameterized by deep neural networks.

We work on marginal generative models, conditional generative models and joint generative models, basing on the following frameworks:

  • Variational Auto-Encoders
  • Diffusion-based models
  • Flow-based models
  • Autoregressive models
  • Energy-based models
  • Score-based generative models

We apply our methods to various problems, e.g., Life Sciences, Molecular Sciences, signal processing, audio synthesis, image/video synthesis, text synthesis.

Our group is involved in teaching and supervision

To all prospective students: We are interested in the theoretical aspects of deep generative modeling, e.g., proposing new models and (preferably) theoretical analysis (e.g., formulating theorems, proving/showing properties). Applications of deep generative modeling are interesting as well, however, we must be aware of limited computational resources at the Univeristy. From our students we expect high independence (including proposing own ideas), good understanding of mathematics (algebra, calculus, statistics, probability theory) and good programming skills (Python + ML/DL libraries, preferably PyTorch). Please take a look at the template of a BSC/MSC thesis and get familiar with information therein.

Currently, we are involved in teaching the following course:

  • Generative AI Models (TU/e)

The goal of our group is to share code together with our research

As a group, we truly believe in reproducible research. Therefore, we publish our code together with papers. You can check our repositories on our Github. If you cannot find a repository there, please visit github repositories of group members.

If any of our papers does not provide code, please contact Jakub.

Available positions in our group

All Ph.D. and postdoc positions in our group must be published at here. Moreover, applying for any positions must be done through this webpage.


For current available positions and projects please contact Jakub.