# On the Apocalypse, programming languages and quantum suicide

In *Good Omens*, Agnes Nutter, Witch, predicts the Apocalypse with extraordinary accuracy: on a specific date, a little bit after tea time. The end of the world is not only a subject of fiction. Serious (and less serious) scientists, politicians, entrepreneurs, and other contemporary prophets have suggested more potential causes than I would dare to count. It might surprise you that there actually is a formula that can predict how long our civilization, or just about anything, will last, and it hardly requires any expert knowledge or complex calculations! The Doomsday Argument is so simple, so elegant that it seems too good to be true. Yet, it is hotly debated among professional and amateur philosophers who can all not quite agree on why it could not possibly hold. This debate is far-reaching since the Doomsday Argument touches all the big questions are pondering about: *will we reach the singularity?*, *are we living in a simulation?*, and *where are all the aliens*?

# Uncertainty propagation and high-dimensional indexing in Julia

# On recovering matrices

# The books of 2018

# Climbing the ladder of causality

# Lessons from convex optimization

In the last four weeks, I taught about convex optimization to my bioinformatics students. Since this topic is of general interest to those working with data and models, I will try to summarize the main points that the ‘casual optimizer’ should know. For a much more comprehensive overview, I refer to the excellent textbook of Boyd and Vandenberghe referenced below.

# The joy of sketchnoting

Since the summer of 2016, I try to frequently make sketchnotes. Sketchnotes are the hipster way of taking notes. The idea is that you document meetings, presentations and the like using a combination of doodles, text and diagrams. That way you end up with a summary that is something a hybrid between a scheme and a comic book page.

# The books of 2017

Looking back, 2017 was an excellent but busy year, both professionally and personally. Luckily, I still found the time to digest some books.

# Sorting socks and other practical uses of algorithms

Algorithms are awesome! While mathematics is mainly involved with proving theorems, which merely state some truth, computer science studies algorithms, which *produce truths*. A mathematician might be able to tell you that there is a way, a computer scientist will be able to find the way!

# Some thoughts on Homo Deus

# Notes on optimal transport

This summer, I stumbled upon the optimal transportation problem, an optimization paradigm where the goal is to transform one probability distribution into another with a minimal cost. It is so simple to understand, yet it has a mind-boggling number of applications in probability, computer vision, machine learning, computational fluid dynamics, and computational biology. I recently gave a seminar on this topic, and this post is an overview of the topic. Slides can be found on my SlideShare and some implementations can are shown in a Jupyter notebook in my Github repo. Enjoy!

# WeGoSTEM

Do you remember the first time you felt like a scientist or an engineer? Yesterday, I had the pleasure of being one of the more than 300 volunteers of the WeGoSTEM project. Jacotte, Luc and myself showed the children Sint-Vincentius school how to build and program a simple drawing robot.

# Seven things I learned during the PhD Cup media school

Whether you like it or not, a large part of a scientist’s job is about communicating. You have to pitch your ideas to collaborators, outline your plans to get grants, educate your students, and report your findings to the scientific community. It is hence a good investment to spend a bit of your precious time honing your soft skills.