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In 2023, I read 87 papers and 26 non-technical books. 87 papers is slightly more than my previous record (82 papers in 2022), and I’ve never even been remotely close to reading 26 books in a year. Deciding to read more books is definitely…

Since I started my PhD almost five years ago, I have categorized, annotated and written short comments for all research papers I read in detail. I share this publicly in a GitHub repository, and recently reached 300 read papers. To mark this milestone, I decided to share some thoughts on why I think it’s important to read a lot of papers, and how I organize my reading. I also compiled some paper statistics, along with a list of 30 papers that I found particularly interesting…

We have created a video in which we try to explain how machine learning works and how it can be used to help doctors. The explanation is tailored to grade 7-9 students, and the idea is that you only should need to know about basic linear functions (straight lines) to understand everything.

When I first got interested in deep learning a couple of years ago, I started out using TensorFlow. In early 2018 I then decided to switch to PyTorch, a decision that I’ve been very happy with ever since…

Please feel free to leave any type of anonymous feedback on my presentation at the following link: https://forms.gle/pfkhxdRzEE7ETmcs8

[Oct 10, 2024] New preprint: Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts [arXiv] [project]. [Oct 1, 2024] New preprint: Evaluating Deep Regression Models for WSI-Based Gene-Expression Prediction [arXiv] [project]. [Sep 16, 2024] New preprint: Taming Diffusion Models for Image Restoration: A Review [arXiv] [project], work lead by Ziwei Luo. [Jul 3, 2024] Accepted paper: Evaluating Regression and Probabilistic Methods for ECG-Based Electrolyte Prediction, work lead together with Philipp Von Bachmann and Daniel Gedon, has been published in Scientific Reports.