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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Members

Jakub M. Tomczak

Associate professor
(Group PI)

Babak Esmaeili

Postdoc

Haotian Chen

Ph.D. candidate

Anna Kuzina

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

Sharvaree Vadgama

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

David W. Romero

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

Adam Izdebski

Ph.D. candidate
(Visiting from the Univ. of Warsaw)

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

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 (not confirmed!):

  • Generative Models
  • Data mining and machine learning

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. Please visit github repositories of group members for more details.

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

Here, we publish all available vacancies in our group

Please check a list below. If there is no announcement, please do not contact us about internships or other positions.

Current available positions and projects (please contact Jakub for details):

  • B.Sc. project in deep generative modeling
  • M.Sc. project in deep generative modeling
  • Ph.D. candidate vacancy (more details coming soon!)