FOSDEM 2026 highlights: security, LLMs, and software performance
Highlights and notes from FOSDEM 2026.
How we won the Amsterdam Policy Hackathon 2025 and what I learned
I participated in the first Amsterdam Policy Hackathon. Here’s how we won our challenge, and what I learned on the way.
Empirically evaluating privacy in machine learning III: functional & gaussian differential privacy
The third in a series of blog posts on machine learning and privacy: why functional differential privacy is useful for auditing privacy in machine learning.
Empirically evaluating privacy in machine learning II: Hypothesis testing
The second in a series of blog posts on machine learning and privacy: Using statistics to think like a hacker, so we can avoid them!
Benchmarking LLM fine-tuning on different HPC systems
We have developed a benchmark that compares the compute performance of fine-tuning LLMs on multiple high-performance computing (HPC) systems, including systems designed for working with sensitive data. In this blog post, we introduce the benchmark, describe the lessons learned developing it and make it open-source so that it can be used and improved by others.
Empirically evaluating privacy in machine learning I: Introduction
The first in a series of blog posts on machine learning and privacy.
FOSDEM 2025 Highlights
Highlights and notes from FOSDEM 2025.
Dependency management and security
Thoughts on dependency management in python and security issues
Contributions to the eScience Center guide
Contributions I made to the Guide of the Netherlands eScience Center.
How to manage your IP address in Python
A tutorial on the sirup package to manage the IP address in python