Collaborators (current)



  • Ewelina Węglarz-Tomczak (NatInLab B.V.): molecular modeling & drug discovery [info]
  • Emiel Hoogeboom (UvA): 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 (UvA): deep learning & machine learning [info]
  • Erik J. Bekkers (UvA): deep learning: equivariance & geometry [info]
  • Alessandro Zonta (VU): computational intelligence & deep learning [info]

PhD students



  1. Anna Kuzina (VU): deep generative modeling and continual learning
  2. David Romero (VU): neural fields, equivariant neural networks, attention
  3. Emile van Krieken (VU): learning algorithms for discrete variables
  4. Sharvaree Vadgama (UvA): deep learning and explainability
 

MSc students

  1. Bartlomiej Boczek (VU): VAEs parameterized by neural fields

Former students



PhD students

  1. Maximilian Ilse [info]: deep learning and causality for medical data, October 14, 2022
  2. Gongjin Lan (VU): learning controllers of evolvable robots, December 16, 2020
  3. Szymon Zaręba [info] (WRUT): deep generative modeling with Restricted Boltzmann Machines, December 13, 2016,
 

MSc students:

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