Collaborators (selected)



  • Ewelina Węglarz-Tomczak (NatInLab B.V.): molecular modeling & drug discovery [info]
  • Emiel Hoogeboom (Google Deepmind): deep generative modeling [info]
  • Guszti Eiben (VU): computational intelligence & robotics [info]
  • Mark Hoogendoorn (VU): machine learning & health applications [info]
  • Annette ten Teije (VU): knowledge representations [info]
  • Max Welling (MSR & UvA): deep learning & machine learning [info]
  • Erik J. Bekkers (UvA): deep learning: equivariance & geometry [info]
  • Adam Izdebski (Univ. of Warsaw/Helmholtz Munich): deep genertive modeling for drug design [info]
  • Ewa Szczurek (Helmholtz Munich): deep genertive modeling for drug design [info]

PhD students



  1. Mahdi Mehmanchi (TU/e): deep generative modeling
  2. Haotian Chen (TU/e): probabilistic modeling
  3. Anna Kuzina (VU): deep generative modeling and continual learning
  4. David Romero (VU): neural fields, equivariant neural networks, attention
  5. Sharvaree Vadgama (UvA): deep learning and explainability
  6. Jan Engelmann (Helmholtz Munich): deep learning fo single-cell data
 

MSc students

  1. Inge Groffen (TU/e): deep generative modeling for molecules
  2. Jasper Linders (TU/e): LLMs + KGs
  3. Dalton Harmsen (TU/e): sustainable LLMs
  4. Marnik Deimann (TU/e): generative segmentation
  5. Dik van Genuchten (TU/e): generative object detecgtion
  6. Sidney Damen (TU/e): deep generative modeling for molecules

Former students



PhD students

  1. Emile van Krieken (VU) [info]: optimization in neurosymbolic learning Systems, January 15, 2024
  2. Maximilian Ilse (UvA) [info]: deep learning and causality for medical data, October 14, 2022
  3. Gongjin Lan (VU) [info]: learning controllers of evolvable robots, December 16, 2020
  4. Szymon Zaręba (WRUT) [info]: deep generative modeling with Restricted Boltzmann Machines, December 13, 2016,
 

MSc students:

  1. Joelle Bink (TU/e): evaluation and prompt engineering for LLMs
  2. Michael Accetto (VU): "Exploration of the internal representation of flow based generative models"
  3. Leonard D. Verbeck (VU): self-supervised computer vision
  4. Jens van Holland (VU)
  5. Sjors Peerdeman (VU)
  6. Ferdi Vestering (VU)
  7. Tom de Valk (VU)
  8. Yi-Ting Lin(VU)
  9. Meagan Tjon Sjoe Sjoe(VU)
  10. Yannick Hogebrug(VU)
  11. Jerry Timmer(VU)
  12. Dorien Verbruggen(VU)
  13. Mats Valk(VU)
  14. Esther Kuikman(VU)
  15. Felix Vink(VU)
  16. Olena Shutko(VU)
  17. Xingkai Wang (VU): Variational Auto-Encoders with reversible computing
  18. Mick Ijzer (VU): Variational Auto-Encoders with non-trainable components
  19. Nihat Uzunalioglu (VU): Generative Attention-based Multiple Instance Learning
  20. Justus Huebotter (VU): spiking neural networks and auto-encoders
  21. Falko Lavitt (VU): Automatic cell counting using deep learning
  22. Ioannis Gatopoulos (UvA-VU): "Self-Supervised Variational Auto-Encoders"
  23. Ilze Auzina (VU): "ABC-Di: Approximate Bayesian Computation for discrete data"
  24. Burcu Kucukoglu (VU-CWI): "Biologically Plausible End-to-end Deep Reinforcement Learning"
  25. Henk van Voorst (UvA)
  26. Tim Davidson (UvA) [info]
  27. Jasper Linmans (UvA) [info]
  28. Philip Botros (UvA) [info]
  29. Marco Federici (UvA) [info]
  30. Szymon Zaręba (PWr) [info]
  31. Przemysław Kłysz (PWr) [info]
  32. Marcin Kocot (PWr)