I’m an assistant professor at the Medical Image Analysis group at Eindhoven University of Technology. Before this I was a postdoc at the Biomedical Imaging Group Rotterdam, Erasmus Medical Center. Before that, I was a PhD student at the Pattern Recognition Laboratory, Delft University of Technology, and a visiting PhD student at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany.
My research focuses on machine learning in medical image analysis. In particular I focus on scenarios where not enough annotated data is available, such as multiple instance learning, transfer learning and crowdsourcing. See my research page for more details.
Another project is my blog, where I write about academic life, failure, getting things done and other tips. Some popular posts are:
- How I Fail series of guests posts
- 7 things I wish I had done during my PhD
- GTD with Todoist and Evernote and Google Calendar
- Planning conference trips with Todoist and Evernote
- Gift ideas for academics
I also like trivia quizzes, beer and cats.
July 2018: eScience-Lorentz workshop “Crowdsourcing in medical image analysis” in Leiden, The Netherlands
19 March 2018: I will give a presentation at ICT.Open in Amersfoort, The Netherlands
A friend of mine nominated me for the TechionistaAwards for inspiring women in STEM and I am now on the shortlist! (Update: I didn’t get the award, but all your messages about voting really helped me, thank you!)
We won the Lorentz-eScience Competition with our proposal for the “Crowdsourcing for Medical Image Analysis” workshop. This is an initiative together with Lora Aroyo (VU), Alessandro Bozzon (Delft University of Technology), Danna Gurari (University of Texas at Austin) and Zoltán Szlavik (IBM Center for Advanced Studies Benelux). Read more about it on the TU/e website
July 2017: Two papers accepted at LABELS 2017: meta-learning in medical image analysis and crowdsourcing in chest CT images
January 2017: New preprint on classification of COPD now available on arXiV
December 2016: Our survey on multiple instance learning is now available on arXiv. Hats off to Marc-André Carbonneau who did most of the work!
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