I'm Timo Kurtz, a computer scientist with a strong focus on Artificial Intelligence, process automation and human-centered software design. I recently completed my M.Sc. in Computer Science with top honors (1.3), supported by a scholarship from HDI. Both my bachelor’s and master’s theses received the highest distinction and explored advanced topics in AI. In my master’s thesis, I developed a large-scale, reasoning-based gold standard dataset for explainability in app reviews, built from over 6.2 million data points and 5,004 multi-annotated samples. I trained and evaluated cutting-edge open and closed-source LLMs including LLaMA, DeepSeek, Gemini and GPT, as well as traditional machine learning models, integrating them into a review analytics tool I designed and developed called feelio. Alongside my academic journey, I’ve released mobile apps, created a personal portfolio website, and realized various side projects that reflect my passion for building meaningful technology. Prior to my studies, I completed a vocational training as an electronics technician for automation at Volkswagen, including a three-month international assignment in Portugal. Professionally, I’ve worked as a Full Stack Developer at Devoteam and as a university tutor in electrical engineering. These experiences have strengthened both my technical expertise and my ability to communicate complex ideas clearly. I also served as a certified IHK ambassador, presenting career paths in tech to young audiences on behalf of Volkswagen. I thrive at the intersection of AI, automation and user experience, with a strong motivation to translate complex technologies into reliable, understandable systems.
During my stay abroad in Portugal I shot and edited two videos. One of the videos is used by Volkswagen as a promotional video.
Volkswagen
A module from the study Computer Science Bachelor of Science at Leibniz University Hannover. Creating a software for the customer.
Leibniz Universität Hannover
feelio is a structured analysis platform for app reviews from the Google Play Store, Apple App Store, and CSV imports. It displays metadata such as average ratings and screenshots, and classifies reviews using AI models into categories such as sentiment, readability, emotion, and type (e.g., bug, UX). The tool offers various visualizations and metrics, supports app comparisons, and allows the flexible integration of new classifiers. It is used in this project to detect explanation needs in reviews and assign them to corresponding taxonomy categories. The software is internally hosted by Leibniz University Hannover; a Figma prototype is available for demonstration purposes.
Leibniz Universität Hannover
In this thesis, a large-scale, reasoning-based gold standard dataset for explanation needs in app reviews was developed—based on over 6.2 million review data points and 5,004 multi-annotated samples from a multi-rater annotation process. Various state-of-the-art LLMs, including LLaMA, DeepSeek, Gemini, and GPT, as well as traditional machine learning models, were trained and evaluated. The best-performing models were integrated into feelio, an interactive analytics tool designed to detect and categorize explanation needs using an extended taxonomy. The software is internally hosted by Leibniz University Hannover. A product presentation is available for demonstration purposes.
Leibniz Universität Hannover
Hanover, Germany
contact@timokurtz.de