Associate professor, IT University of Copenhagen, 2021-current

Assistant professor, Eindhoven University of Technology, 2017-2020. 

Postdoctoral researcher, Erasmus Medical Center, 2015-2016

Visiting researcher, MPI Intelligent Systems, Tübingen, Germany, 2013

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. 

Programmer (part-time), 2006-2010. Web development at Dreamsolution, software testing at Tjip, freelaance projects.

Grants and awards

Research funding

  • 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 CATSChoosing 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


ITU Copenhagen

  • Seminar Data Science (MSc), Autumn 2022. Lecturer
  • Data in the Wild (MSc), Autumn 2022. Responsible instructor/ lecturer
  • First year project (BSc), Spring 2022. Responsible instructor / lecturer
  • Research communication (PhD course), Autumn 2021. Guest lecturer
  • Advanced Database Systems (MSc), Autumn 2021. Guest lecturer
  • Seminar Data Science (MSc), Autumn 2021. Lecturer
  • Data in the Wild (MSc), Autumn 2021. Responsible instructor/ lecturer
  • First year project (BSc), Spring 2021. Lecturer

TU Eindhoven

Erasmus Medical Center

  • Introduction to medical imaging, 2015-2016. MSc course for medical students, 1-2 lectures per year

TU Delft


Supervision PhD researchers

  • Dovile Juodelyte (2022 – current) – choosing datasets for transfer learning
  • Bethany Chamberlain (2022 – current) – choosing datasets for transfer learning
  • Ralf Raumanns (2019 – current) – multi-task learning for explainable AI
  • Ishaan Bhat (2019 – 2020) – 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

  • Richard Bortsov, Erasmus Medical Center, The Netherlands, 2022
  • 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

  • Pernille Matthews (SDU, 2022), Maude Helen Plucker (KU, 2022)
  • 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

Professional service

Organization leadership

  • 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


A verified record of my reviews can be found on my Publons page.

Grant reviewing

  • Hartstichting (Dutch Heart Foundation) – 2021
  • ANR (French National Research Agency) – 2021
  • Astra Zeneca grant – 2020
  • ETH postdoc fellowship – 2018
  • NSERC (National Sciences and Research Council of Canada) – 2017


Please see this page or my Google Scholar page for an up-to-date list of publications.


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 ( 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:

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