Hi there!

My name is Peter Weinberger and this nice little website is about me. Of course you don’t know me. Therefore, I take the chance and introduce myself briefly.

Before I started my academic endeavour, I have completed an apprenticeship as computer science expert specialised in application development (Fachinformatiker Fachrichtung Anwendungsentwicklung) at Nürnberger Versicherungsgruppe AG.

During my Bachelor’s I kept my coding expertise updated as a working student at Siemens and Immowelt. Additionally, I was a Data Scientist intern at DATEV Innovation Lab and created NLP proof-of-concepts there.

My academic career began with my bachelor studies in Computer Science at the Nuremberg Institute of Technology. During my bachelor’s degree, I focused on Machine Learning and developed an ever-growing enthusiasm for this subject area. Extracting new information from existing data has fascinated me ever since.

My passion is the reason why I decided to take up the Data Engineering and Analytics Master’s programme in the summer semester 2020 at the Technical University of Munich. Throughout my studies at TUM, I specialized in Natural Language Processing (NLP) and gained hands-on experience while developing a semantic search engine using cutting-edge transformer Deep Learning models at TUM Data Innovation Lab in cooperation with Horváth & Partners. Furthermore, I completed a guided research where I evaluated semantic linking capabilities of bilingual engineering-specific word embeddings. The findings of my research have been published at MSIE 2022.

Besides my enthusiasm for NLP I am also really into Deep Learning. Therefore, I passed “Introduction to Deep Learning” and completed the practical course “Creation of Deep Learning Methods”. For the latter, I had to analyse various Deep Learning architectures in order to be able to understand why an architecture is suitable for solving a specific use case. Moreover, I applied my knowledge from NLP to Computer Vision in my Master’s thesis “Medical Image Segmentation Using Self-Supervised Learning and Vision Transformers” under the supervision of Prof. Dr. Nassir Navab (Johns Hopkins University and TUM). For my thesis, I created a novel Deep Learning architecture using features stemming from pre-trained and fine-tuned Vision Transformers as an additional input. My Deep Learning architecture outperforms latest state-of-the-art transformer-based models and will be published as a conference paper at CVPR 2023 (currently under review).

In my Master’s I also had to keep up my slogan “Always pass on what you have learned.". For that reason, I was a Teaching Assistant for “Foundations in Data Engineering” in the winter semester 2021/2022. This course is mandatory for Data Engineering and Analytics students and I really enjoyed it. My responsibilities included teaching tutorials on advanced SQL, distributed computing, MapReduce, Apache Spark, and C++ performance optimization.

While finishing up my Master’s till today, I intern at Amazon in Luxembourg as a Business Intelligence Engineer. In my daily work, I discover patterns in Amazons' enormous data streams to keep the high level of quality in the delivery supply chain up.

Currently, I am searching for interesting full-time positions as a Data Scientist or Machine Learning Engineer.

TL;DR

Here is my resume.