
Fourier Neural Operators (FNOs)
A step by step guide through the architecture of Fourier Neural Operators, their applications, and how they efficiently process global information through the Fourier transform.

a CS master's student focused on deep learning for the simulation of physical systems — and a student AI & Data Scientist at Statista+.
2024 — today
M.Sc. Computer Science · AI & Data Science
HAW Hamburg
02/2026 — 07/2026
Postgraduate Exchange Semester Scholarship
UNSW Sydney
2020 — 2024
B.Sc. Media Computer Science
University of Flensburg
2025 — today
Working Student · AI & Data Science
Statista
2021 — 2025
Working Student · Fullstack Development
Jung von Matt TECH, SOFTSTACK, Events United
Deutschlandstipendium
Scholarship for Gifted and High-Achieving Students

A step by step guide through the architecture of Fourier Neural Operators, their applications, and how they efficiently process global information through the Fourier transform.

A semi-deep dive into the Fourier transform and its applications in signal processing and neural networks.

An introduction to neural operators, DeepONet, and how they can be used to learn mappings between function spaces.

A deep dive into the Von Neumann stability analysis of the wave equation and its connection to the CFL condition.

A deep dive into the von Neumann stability analysis, its connection to the CFL condition, and how it determines the stability of numerical schemes.

A deep dive into the wave equation, its reformulation for neural network approximation, and the challenges of learning dynamics with finite speed.
A MIDI Event based transformer architecture, utilizing two seperate transformers operating on the global MIDI events and local event parameters.
A basic transformer architecture for MIDI generation, utilizing a single decoder to enable faster training and inference speed.
A real-time streaming pipeline correlating German news sentiment (GDELT) with Spotify Top 200 musical features, using Kafka and Spark Streaming for dual-stream aggregation.