- Ph.D. in Computer Science, Delft University of Technology, 2011-2015. Thesis “Dissimilarity-Based Multiple Instance Learning”
- M.Sc. in Computer Science, Delft University of Technology, 2008-2010. Thesis “Random Subspace Method for One-Class Classifiers”
- B.Sc. in Computer Science, Delft University of Technology, 2004-2007
- International Baccalaureate, International School of The Hague, 1999-2004
Associate professor, IT University of Copenhagen, 2021-current
Assistant professor, Eindhoven University of Technology, 2017-2020.
- Part of Medical Image Analysis group
- Research on learning with limited labeled data
- Received University Teaching Qualification (2019)
Postdoctoral researcher, Erasmus Medical Center, 2015-2016
- Part of Biomedical Imaging Group Rotterdam
- Project: Transfer learning in medical image analysis
Visiting researcher, MPI Intelligent Systems, Tübingen, Germany, 2013
- Visited Machine Learning and Computational Biology group (now at ETH Zurich)
- Advisor: Dr. Aasa Feragen
Ph.D. researcher, Delft University of Technology, The Netherlands, 2011-2015
- Part of Pattern Recognition Laboratory
- Project: Dissimilarity-Based Multiple Instance Learning
- Advisors: Prof. dr. Marco Loog and Dr. David M. J. Tax
M.Sc. intern Prime Vision, Delft, The Netherlands, 2010. Project on outlier detection.
Grants and awards
- Van Gogh grant (EUR 6K) for collaboration with University of Rouen, France (2020)
- NWO Lerarenbeurs (2019), ~ EUR 250K for a project on multi-task learning for explainable AI. Main applicant Ralf Raumanns.
- Mozilla Open Science mini-grant (2019), USD 10K for organizing a workshop on open and inclusive academia
- TU Eindhoven Education Innovation funding (2018), EUR 120K internal funding for projects to improve medical image analysis courses (together with Dr. Mitko Veta and Dr. Joaquin Vanschoren).
- NVIDIA GPU Grant (2018), Titan XP GPU worth USD 1149.99
- Winner Lorentz-eScience competition 2017, EUR 15K for organizing a workshop “Crowdsourcing for medical image analysis”
- Winner Pallas Ludens Academic Challenge, 2016, EUR 5K for crowdsourcing project
- Travel scholarship Delft University of Technology, 2013, EUR 2.5K for visiting MPI Tuebingen for 3 months
- Travel scholarship ACM-W/Microsoft, 2013. EUR 1.2K for visiting Multiple Classifier Systems conference in Nanjing, China
- Second place in Publons ECR Reviewer awards 2018
- Shortlisted for Techionista awards 2017
- Finalist STW Open Mind Award, 2015 for “Game for crowdsourcing medical image analysis”. Top 15 of 132 applicants
- Member of merit award, W.I.S.V. `Christiaan Huygens’, 2009. Recognized for my contribution to the society for mathematics and computer science students.
- 8DM20 Capita Selecta medical imaging, 2020. MSc course, lectures on semi-supervised and transfer learning
- 8QA01 image analysis project, 2017-2020. Responsible instructor / lecturer, 1st year BSc course.
- 8DC00 medical image analysis, 2017-2019. Instructor / lecturer, together with Dr. Mitko Veta, 3rd year BSc course.
Erasmus Medical Center
- Introduction to medical imaging, 2015-2016. MSc course for medical students, 1-2 lectures per year
- Advanced pattern recognition, 2014-2016. Lecturer/teaching assistant in intensive course for PhD students.
- Pattern recognition, 2011-2014. Lecturer (2 lectures) in BSc course for biomedical engineering students, teaching assistant in MSc course for computer science
- Algorithms and data structures, 2007. Teaching assistant in BSc course.
Supervision PhD researchers
- Ralf Raumanns (2019 – current) – project on multi-task learning for explainable AI
- Ishaan Bhat (2019 – 2020) – project on learning from limited labeled data in liver MRI. Ishaan transferred to a different supervisor due to me leaving TU Eindhoven.
Supervision MSc & visiting students
- Irma van den Brandt (2020, internship) – Transfer learning from (non)-medical datasets
- Britt Michels (2019-2020) – Evaluation of medical image segmentation
- Colin Nieuwlaat (2019-2020) – Transfer learning & dataset shift
- Rumjana Romanova (2019-2020) – Stability of deep multiple instance learning
- Tom van Sonsbeek (2018-2019) – Meta-learning for medical image segmentation
- Linde Hesse (2018-2019) – Lung nodule detection in spectral CT
- Elif Kübra Çontar (2018, internship) – Melanoma classification with crowdsourced labels
Supervision BSc students
- Bas Mulders, Felix Schijve (2020) – Meta-features for (non)-medical datasets
- Thaomi Tran, Max Joosten (2020) – Multi-task learning with crowdsourced features
- Ivana Rovers, Francoise Brouckaert (2020) – Uncertainty in liver segmentation
- Marjolein Pijper (2019) – Semi-supervised learning for liver segmentation
- Audrey Pfeiffer, Sanne Mevissen, Emiel Roefs (2019) – Feature extraction for skin lesion images
- Floris Fok (2018) – Transfer learning from non-medical images
- Merel Eussen (2018) – Crowdsourcing in chest CT images
- Marco van Tilburg, Timo van Limpt (2018) – Crowdsourcing for registration in chest CT
- Dylan Dophemont (2016) – A purposeful game for medical image annotation
PhD defense committee
- Annegreet van Opbroek, “Transfer learning for medical image segmentation”, Erasmus Medical Center, 2018
- Jean-Paul Charbonnier, “Segmentation and quantification of airways and blood vessels in chest CT”, University of Nijmegen, 2017
MSc defense committee
- Teun van Westenberg, Anouk van Rijn, Luc Hendriks, Vera Vos, Evi Huijben (2020)
- Daan Meister (2019), Marloes Adonk (2019, TU Delft)
- Tim Beishuizen, Mark Janse, Jesper Pilmeyer, Pascal van Beek (2018)
- Carlijn Bakker, Shefali Chand (2017)
- Dimitrios Palachanis (2014, TU Delft)
- Gijs van Tulder (2012, TU Delft)
I’m part of a formal mentorship program for MSc students at Eindhoven University of Technology, where I’m mentoring several students per year:
- Yi He Zhu, Robert van Dijk, Naomi de Kruif (2019 – )
- Renate Doormaal, Dieter Timmers (2018 – )
- Britt Michels, Evi Huijben (2017 – 2020)
- Social media chair MICCAI 2020 (largest medical imaging conference), 2019-2020.
- Committee member Women in MICCAI, 2017-2018, committee to strengthen the representation of female scientists in medical imaging
- Board member NVPHBV (Dutch society of Pattern Recognition and Image Processing), 2014-current. Treasurer between 2014-2018.
- Board and committee member W.I.S.V. ‘Christiaan Huygens’, society for computer science and mathematics students. Full-time board member (2007-2008), supervising committees in organizing events.
Workshop chair or co-chair
- Avengers for Better Science, Leiden, The Netherlands, 2019
- Large-scale Annotation of Biomedical data and Expert Label Synthesis (MICCAI LABELS), Granada, Spain, 2018
- Crowdsourcing for Medical Image Analysis, Leiden, The Netherlands, 2018
- Large-scale Annotation of Biomedical data and Expert Label Synthesis (MICCAI LABELS), Quebec City, Canada, 2017
- Meetings of the Dutch Society of Pattern Recognition and Image Processing (NVPHBV), The Netherlands. 50-60 participants each
- Features and Structures (FEAST) workshop at International Conference on Machine Learning (ICML 2015), Lille, France. 170 registered participants.
- Features and Structures (FEAST) workshop at International Conference on Pattern Recognition (ICPR 2014), Stockholm, Sweden. 70 registered participants
- Associate editor Pattern Recognition 2019-2020
- Area chair for MICCAI 2019
- I have reviewed over 130 articles for international journals and conferences, such as Nature Communications, IEEE TPAMI, IEEE TMI, IEEE TNNLS, Pattern Recognition, MICCAI, ISBI, NeurIPS and others.
A verified record of my reviews can be found on my Publons page.
- Astra Zeneca grant (2020)
- ETH postdoc fellowship (2018)
I have given several invited and contributed talks at international and national conferences. I have also given several talks focusing on outreach and career development. For a full list please see this page.
I’ve given various career orientation talks to high-school and university students, and early career researchers. From 2014 to 2019 I was also giving talks at events organized by VHTO (Dutch national organization on girls/women and science and technology) which encourages girls to study technical subjects.