Lukas F. Münzel

Hi there!

My main goal is to have as much positive impact as I can. I believe that the best and most interesting way to achieve this is to actively contribute to progress in machine learning. Indeed, I believe that AGI this decade or certainly within my lifetime is plausible and, if achieved, will enable rates of progress dwarfing anything we have lived through so far.

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

Cheers,

Lukas

I set up a feedback form. If there is anything you’d like to anonymously tell me, I’d be grateful if you let me know :)

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

some of my work

A small observation on Schulmann’s KL divergence estimator

Lukas Münzel

May 2025

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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,
}