Hi, my name is
Jonas Zausinger
I am studying M.Sc. Computer Science at the Technical University of Munich.
Besides I am a participant in the AI Career Kickstart Program from AppliedAI and I am currently doing a five-month internship at Bosch as part of this programme.
My main interests are machine learning, data analysis as well as backend development.
About Me
Hello, my name is Jonas and I have been studying computer science since 2018. I completed my bachelor's degree at the University of Passau in 2021.
Since 2022, I am now studying for a Master's degree at the Technical University of Munich with a focus on Machine Learning and Data Analysis.
From 2019 to 2023, I worked as a working student at the software development and consulting company IAMDS.
During this time, I was involved in a variety of projects ranging from web development and machine learning to building the IAMDS Dataplatform.
I then spent nine months as a working student at Siemens, where I took on exciting tasks in areas such as anomaly detection and data analysis of various industrial data. I am currently taking part in the AI Career Kickstart Program from AppliedAI and am doing an internship at Bosch as part of this. There I am working on the application of large language models and the solution of time series problems in the field of machine learning.
I have worked with these technologies in particular:
- Python
- JavaScript
- Java
- SQL
- React
- Spring
- Tensorflow
- Vue
- Kubernetes
- Pytorch
- Node.js
- Docker
- Kafka
Projects as a working student
EPM-App
August 2023 - January 2024
- Python
- Mqtt
- Docker
- Edge Computing
- Migration of a monitoring and anomaly detection application for wired arc additive manufacturing to the Siemens Industrial Edge platform
- Setting up the Industrial Edge System for the local application
- Customisation of various microservices for the Edge Platform
Exciting projects
Utilizing Large Language Models for Causal Discovery and Legal Text Interpretation: A Case Study on the German GoZ
In this project I developed a methodology to automatically extract causal relationships and interpret rules from legal texts using Large Language Models (LLMs). The focus is on the automatic extraction of rules from the German Dentist Fee Schedule (GoZ), and their automatic application to dental invoices.
- Python
- Langchain
- Large Language Models
- Prolog
Automatic evaluation of Corona rapid tests
Development of a process for the automatic evaluation of Corona rapid tests and the image recognition AI required for this. Automatic evaluation of hundreds of Corona quick tests daily.
- Python
- Tensorflow
- Node.js
- Vue.js
Data Explorer
Development of a data visualization tool with algorithmic execution capabilities as part of my bachelor thesis. The application was published as part of a scientific paper at the AllData23 conference.
The application facilitates access to and experimentation with data by allowing users to visualize datasets and perform and reproduce data processing steps. Its purpose is to lower the barrier to entry when working with unfamiliar datasets by providing the ability to quickly run small tests, view the code of the algorithms along with the visualizations of the data, and thus facilitate the move to your own experiments/applications with the data.
- JavaScript
- JQery
- Python
- HTML
- CSS
Style NeRF
Implementation of Artistic Style Transfer on 3D Scenes by using Neural Radiance Field (NeRF), a neural network for rendering 3D Scenes in a team of two people.
- Python
- Pytorch
- NeRF
Blackjack
Development of the card game Blackjack as a browser game with a system for calculating the optimal game strategy.
- React.js
- Typescript
HyperLogistic
Development of an application for managing and solving logistics problems by means of automatic optimization through simulation software and machine learning in a team of six people.
- Python
- Flask
- XGBoost
- Pytorch
- Simpy
