Explaining metastable behavior using data-driven methods
Designing and building a splitkeyboard from hardware to firmware!
How the pandemic spread through the Netherlands. Click for pretty animations!
How roschambo can generate to fluid-like pattern.
Power laws are everywhere, even in your coffee! One lazy sunday morning I wondered whether the bubbles in the milk of my coffee was power law distributed; a little bit of coding and a few moments later we had an answer!
Given the skyrocketing mobile phone prices, I wondered: ‘How many iPhones can I buy if I live in Australia?’
Fast computational framework for complex adaptive systems simulations
Webscraping woningnet and providing an friendly userinterface
A general toolbox for analyzing discrete complex dynamic systems using information theory.
A python daemon around tss-saver
A fast 3D viewer for analyzing large scale brain data.
Some lab reports part of master the course statistical machine learning.
Various simple computational models ranging from Ising model, cellular automatons (1D and 2D), Izhikevich neurons, and the Nubian sandpile.
Abrupt, system-wide transitions can be endogenously generated by seemingly stable networks of interacting dynamical units, such as mode switching in neuronal networks or public opinion changes in social systems. However, it remains poorly understood how such `noise-induced transitions’ are generated by the interplay of network structure and dynamics on the network. We identify two key roles for nodes on how tipping points can emerge in dynamical networks governed by the Boltzmann-Gibbs distribution. In the initial phase, initiator nodes absorb and transmit short-lived fluctuations to neighboring nodes, causing a domino-effect making neighboring nodes more dynamic. Conversely, towards the tipping point we identify stabilizer nodes whose state information becomes part of the long-term memory of the system. We validate these roles by targeted interventions that make tipping points more (less) likely to begin or lead to systemic change. This opens up possibilities for understanding and controlling endogenously generated metastable behavior.