Hi There!

I love tinkering with hard problems and algorithms. I’ve worked in computational neuroscience at Maastricht University on biophysical models of the human brain, developing methods for analyzing complex spatio-temporal neurobiological data. A lot of things interest me and this is the place where I organize my ideas and share what I learn along the way.

I write about a wide range of topics and projects — from optimizing biophysics simulations and building data-analysis pipelines, to exploring machine learning techniques and interesting algorithmic challenges. I’m especially drawn to problems where mathematical insight meets practical implementation.

If you’re working on related problems and think we might have something to discuss, I’d love to hear from you!


Recent Posts

Representing Multi-Scale Systems using Directed Acyclic Graphs

August 07, 2025

Implementation of a data-oriented data structure: a directed acyclic graph. The data structure can be used to build large systems of many hierarchically related components while keeping cache-friendly memory allocations.
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Data-Oriented Design in Biology

February 25, 2025

Data oriented programming principles are good practise in software development. Here I set out to describe how such principles could aid in building better models, with a particular focus on my own field - biophysics.
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Simulation Based Inference on the Lorenz System

May 25, 2024

Short introduction and walkthrough on how to use simulation based inference with an example using the Lorenz system.
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A Python tool for Streamlining Parameter Search in Computational Biology

September 03, 2023

Orca provides tools for sequentially running generative models to obtain a well organized dataset of parameters and their associated observables from model simulations.
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SnakeMake