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.
I participated in the first Amsterdam Policy Hackathon. Here’s how we won our challenge, and what I learned on the way.
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.
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!
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.
The first in a series of blog posts on machine learning and privacy.
Highlights and notes from FOSDEM 2025
Thoughts on dependency management in python and security issues
Contributions I made to the Guide of the Netherlands eScience Center.
A tutorial on the sirup package to manage the IP address in python
Describes my experience from using the mock module with pytest for mocking calls to external functions.