Lukas F. Münzel

Hi there!

I want to have as much positive impact as I can.

Axioms:

We will with high probability achieve AGI ¹ in my lifetime
Scientific progress allows us to enormously improve people’s lives

Corollary: We should use AGI to massively accelerate research while ensuring that the gains we obtain from that for good. On a more personal note, this will also be incredibly cool

Also I really love meeting new interesting people! Thus, if anything on this website seems interesting to you, you are more than./ welcome to write me a mail and we can gladly go for lunch or a coffee.

Cheers,

Lukas

I am really grateful for anonymous feedback :)

Short summary of my CV:

In 2021, I had the pleasure of participating in the Research Science Institute, a six-week research program held at MIT with amazing people from all over the world
I participated in the Informatics Olympiad with reasonable success, being one of the four people to represent Switzerland internationally in 2020 and 2022 respectively. I also qualified for the final rounds of the Swiss Biology and Physics Olympiad

a system that can completely autonomously do any task John von Neumann could complete given a blackboard, a computer, and ten years ↩

some of my work

A small observation on Schulmann’s KL divergence estimator

Lukas Münzel

May 2025

View
Fast monotonically integrated large eddy simulation solver: validation of a new scalable tool to study and optimize indoor ventilation

N.M. Leuenberger, R. Yang, L. Münzel, R. Verzicco, D. Lohse, and 2 more authors

Flow, May 2024

PDF
Radiograph Automata - A novel model of computation

Lukas Münzel

Jul 2023

PDF
The Influence of the Orthogonality of Feature Functions on Artificial Neural Networks

Lukas Münzel

Oct 2021

Abs PDF (English) PDF (German) Presentation recording

Conventional methods for adjusting the weights of artificial neural networks only consider the output layer when calculating the loss. Our method also takes into account infor-mation from the functions generating the outputs of the neurons in the second to last layer,which are called feature functions. Specifically, we measure how orthogonal the feature functions are using a covariance measure. We incorporate a term in the loss to explicitly rewardorthogonal feature functions. We were then able to generate nearly orthogonal feature functions without compromising on accuracy. Due to the convenient mathematical properties oforthogonal feature functions, we will in a future next step be able to find the optimal choiceof weights between the second to last and the output layer with low computational cost.

To cuckold and to be cuckolded - An essay on parallels between Shakespeare’s Othello and Greek mythology

Lukas Münzel

Nov 2021

Bib PDF

@article{paralells_othello_odyssey,
  title = {To cuckold and to be cuckolded - An essay on parallels between Shakespeare’s Othello and Greek mythology},
  author = {Münzel, Lukas},
  year = {2021},
  month = nov,
}