About me
I'm a PhD candidate in the Diagnostic Image Analysis Group at Radboudumc, Nijmegen, where I work on deep learning for pulmonary nodule malignancy estimation in chest CT. Before that, I worked as a data scientist and studied biomedical sciences and AI. What draws me to this field is the combination: building models is one thing, but making them safe and trustworthy enough for a radiologist to rely on is the real challenge.
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Python
My primary programming language, used daily. I mainly work with NumPy, Pandas, Matplotlib, scikit-learn, and PyTorch for tasks ranging from data analysis to deep learning, and with Requests and BeautifulSoup for web scraping.
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Artificial intelligence
Experience developing machine and deep learning algorithms, from linear regression to multi-view ResNets, transformers, and multi-task models.
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Medical imaging
Since 2019 I specialize in medical image analysis, specifically in radiology, working with 2D and 3D modalities including phantom, X-ray, and CT images.
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Git & GitHub
I use Git for version control in all projects and software development, with GitHub for collaboration and CI.
- 2022 — present PhD candidate Radboudumc, Nijmegen
- 2021 — 2022 Data scientist Mentech, Eindhoven
- 2020 — 2021 Data science intern LRCB, Nijmegen
- 2019 — 2020 Data science intern The D-Lab, Maastricht
- 2018 — 2020 Electrical engineering (student job) Dinnissen Process Technology, Sevenum
- MSc Biomedical Sciences and Artificial Intelligence Maastricht University
- BSc Biometrics Zuyd University of Applied Sciences
- Traineeship Data Engineering Closesure (League of Talents)
- AWS Technical Essentials AWS
- Microsoft Azure Fundamentals (AZ-900) Microsoft
- Convolutional Neural Networks Coursera
- Neural Networks and Deep Learning Coursera
- Intelligent Systems in Medical Imaging Radboudumc
- Scientific Integrity Radboud University
- Writing Scientific Articles Radboud University
- Project Management Radboud University
- Agile Foundation Agile Consortium