- 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
- Part of DASYA group
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
- Inge Lehmann grant 2021 (DKK 2.8M, ~EUR 380K) for project “Making Metadata Count” [News]
- Starting package Novo Nordisk 2021 (DKK 3.5M, ~EUR 470K) for project CATS: Choosing A Transfer Source for medical image classification
- NWO Lerarenbeurs (2019), ~ EUR 250K for a project on multi-task learning for explainable AI. Main applicant Ralf Raumanns
Other awarded grants
- Van Gogh grant (EUR 6K) for collaboration with University of Rouen, France (2020)
- 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
Awards / honorable mentions
- 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.
- Seminar Data Science, Autumn 2021. Lecturer, MSc Data Science
- Data in the Wild, Autumn 2021. Responsible instructor/ lecturer, MSc Data Science.
- First year project, Spring 2021. Lecturer, BSc Data Science.
- 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 theses
- Nikolaj Kjøller Bjerregaard (2022) – Detecting furigana in Japanese media
- 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
Supervision BSc theses
- Kasper Thorhauge Grønbek, Andreas Skovdal (2022) – Hidden features in chest X-ray images
- Frederik Bechmann Faarup (2022) – Hidden features in mammography images
- 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
- Elif Kübra Çontar (2018, internship) – Melanoma classification with crowdsourced labels
- 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
- Jeppe Thagaard, Technical University of Denmark, Denmark, 2021
- Marco Domenico Cirillo, Linköping University, Sweden, 2021 (Opponent)
- Rosana El Jurdi, University of Rouen, France, 2021
- Annegreet van Opbroek, “Transfer learning for medical image segmentation”, Erasmus Medical Center, The Netherlands, 2018
- Jean-Paul Charbonnier, “Segmentation and quantification of airways and blood vessels in chest CT”, University of Nijmegen, The Netherlands, 2017
MSc defense committee
- TU Eindhoven: Teun van Westenberg, Anouk van Rijn, Luc Hendriks, Vera Vos, Evi Huijben (all 2020), Daan Meister (2019), Tim Beishuizen, Mark Janse, Jesper Pilmeyer, Pascal van Beek (all 2018), Carlijn Bakker, Shefali Chand (2017)
- TU Delft: Marloes Adonk (2019), Dimitrios Palachanis (2014), Gijs van Tulder (2012)
I was part of a formal mentorship program for MSc students at Eindhoven University of Technology, where I mentored the following students between 2017 and 2020: Yi He Zhu, Robert van Dijk, Naomi de Kruif, Renate Doormaal, Dieter Timmers, Britt Michels, Evi Huijben
- 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
- Area chair for MIDL 2022
- 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.
- Hartstichting (Dutch Heart Foundation) – 2021
- ANR (French National Research Agency) – 2021
- 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 use this website (http://www.veronikach.com) and Twitter (@drveronikach) to communicate about my research and about academic life, including via VHTO (Dutch national organization on girls/women and science and technology).
My work has been featured in various articles, podcasts etc:
- PhDTalk podcast
- Interview with Eindhovens Dagblad (in Dutch)
- How I Fail series on the NaturalScience.Careers podcast
- De Ingenieur interviewed me about failure for their March 2020 issue (in Dutch)
- I wrote about failure in academia for The Times Higher Education
- ProfHacker blog featured my “How I Fail” series
- Personal Finance for PhDs podcast
- WithAScienceDegree interview series
- WeTalkScience blog post series
- Software Engineering Radio podcast
- Chemical & Engineering news
- MeetTheScholar interview series
- How I Work interview series