CV

Education

Certifications

Experience

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 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

Teaching

ITU Copenhagen

  • Data in the Wild (MSc), Fall 2021, 2022, 2023. Responsible instructor/ lecturer
  • Seminar Data Science (MSc), Fall 2021, 2022, 2023. Lecturer
  • First year project (BSc), Spring 2021, 2022, 2023. Responsible instructor / lecturer
  • Research communication (PhD course), Autumn 2021. Guest lecturer
  • Advanced Database Systems (MSc), Autumn 2021. Guest lecturer

TU Eindhoven

Erasmus Medical Center

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

TU Delft

Advising

PhD researchers & postdocs

  • Amelia Jiménez-Sánchez (2022 – current) – shortcuts in medical imaging
  • Dovile Juodelyte (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 students

  • Stinna Ødgaard Winther (2023) – Public chest x-ray datasets
  • Cathrine Damgaard, Trine Naja Eriksen (2023) – Hidden features in chest x-rays
  • Théo Sourget (2023, visiting student) – Public datasets for medical image segmentation
  • Ahmet Akkoc (2022) – Dataset use in medical image conferences
  • Christine Kaarde Galsgaard (2022) – Values in MICCAI papers
  • Nikolaj Kjøller Bjerregaard (2022) – Detecting furigana in Japanese media
  • Irma van den Brandt (2020) – 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 students

  • Danielle Marie Dequin, Chrisanna Kate Cornish (2023) – Carbon footprint of Kaggle
  • Jacob Andreas Sneding Rohde (2023) – Detecting fungus in construction images
  • 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, visiting student) – 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

Professional service

Board or committees

  • Co-founder of the Danish Reproducibility Network, 2023-
  • Research Ethics Committee at IT University of Copenhagen, 2023-
  • 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.
  • W.I.S.V. ‘Christiaan Huygens’, society for computer science and mathematics students. Full-time board member (2007-2008)

Workshop organization

Reviewing

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

Grant reviewing

  • Israel Ministry of Science and Technology – 2022
  • Hartstichting (Dutch Heart Foundation) – 2021
  • ANR (French National Research Agency) – 2021
  • FWO (Belgian Research Foundation) / Astra Zeneca – 2020
  • ETH postdoc fellowship – 2018
  • NSERC (National Sciences and Research Council of Canada) – 2017

Publications

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

Talks

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.

Outreach

I use this website (http://www.veronikach.com) and social media to communicate about my research and about academic life. My work has been featured in various articles, podcasts etc:

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