Collaborators (selected)



  • Ewelina Węglarz-Tomczak (NatInLab B.V.): molecular modeling & drug discovery [info]
  • Emiel Hoogeboom (Google Deepmind): deep generative modeling [info]
  • Max Welling (UvA): deep learning & machine learning [info]
  • F. Paolo Casale (Helmholtz Munich): deep genertive modeling for singe-cell data [info]
  • Erik J. Bekkers (UvA): deep learning: equivariance & geometry [info]
  • Mark Hoogendoorn (VU): machine learning & health applications [info]
  • Adam Izdebski (Univ. of Warsaw/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. Sharvaree Vadgama (UvA): deep learning and explainability
  5. Jan Engelmann (Helmholtz Munich): deep learning fo single-cell data
 

MSc students

  1. Jasper Linders (TU/e): LLMs + KGs

Former students



PhD students

  1. David Romero (VU) [info]: efficient deep learning (equivariance & implicit representations), September 10, 2024
  2. Jie Luo (VU) [info]: learning in evolutionary robotics, September 10, 2024
  3. Emile van Krieken (VU) [info]: optimization in neurosymbolic learning Systems, January 15, 2024
  4. Maximilian Ilse (UvA) [info]: deep learning and causality for medical data, October 14, 2022
  5. Gongjin Lan (VU) [info]: learning controllers of evolvable robots, December 16, 2020
  6. Szymon Zaręba (WRUT) [info]: deep generative modeling with Restricted Boltzmann Machines, December 13, 2016,
 

MSc students:

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