Ongoing: Mathematics Bachelor's Thesis
My second Bachelor’s Thesis deals with Latent Space Optimization done via generative models. The work is in early stage (as of February 2022).
Ongoing: Telegram crawler to analyze German conspiracy theory chats
During the COVID-19 Pandemic, a lot of conspiracy theorists gathered on Telegram in various public groups and channels. I’m working on a crawler, the task of which is to scan these chats and analyze the public available information. At the time of writing (Jan 2022), the crawler collected >20 GB worth of text messages, 1.75 mio user accounts and >5300 German chats, the vast majority of which is promoting conspiracy theories.
pyAnt: RTS Serious Game
pyAnt is a Serious Game (SG) prototype, the concept of which is to build an ant colony where the ants are controlled only via Python code. I developed that game together with my team as a project at the SG group. We implemented the game with the Godot engine and released it on itch.io.
Learning Angry Birds with RL
I built a TensorFlow framework for learning Angry Birds (and other games) using basic state-of-the-art Reinforcement Learning (RL) techniques. One can easily exchange the AI model and the game environment. This is my largest project so far and is a continuation of a bonus project I finished together with three other team members.
Explainable AI: A systematic review to find a unified definition of explainability and interpretability
A scientific literature analysis I’ve done with a colleague for a seminar. Commonly used AI models, especially neural networks, are hard to explain and to understand. In order to support trust, explainability of these models is a desired property. However, there is no unique definition of explainability and interpretability. We examined the different definitions stated in the literature and, based on that, proposed an own one.
SinGAN: Train a GAN with only a single image
This is an implementation of the SinGAN presented in this paper. It is a bonus project I coded with a colleague in PyTorch. It can be used to train a GAN on just a single image. That GAN can then perform a bunch of tasks, e.g., super resolution or image inpainting. This project whips with a Django frontend ready to be deployed as an interactive webpage.
Fooling an image classifier with adversarial inputs
This little experiment showcases how easy neural network image classifiers can be fooled with a slight perturbation in the image. It also explains and shows Saliency Maps (heatmaps indicating the classifier’s attention in the image when classifying it).
Interpreting Sum-Product Networks via Influence Functions
As my Computer Science Bachelor’s Thesis, I investigated Sum-Product Networks (SPNs) using so-called Influence Functions. My work contributes to the understanding of SPNs by showing, e.g., how single training instances affect the SPN’s prediction on other instances.