I wrote my Master's thesis at the Institute of Man-Machine Interaction at RWTH Aachen, evaluating whether generative image synthesis can serve as a computationally efficient alternative to ray-traced rendering in simulation pipelines. I trained and evaluated a GAN-based and a diffusion-based model on this task. The GAN-based model generated frames about 1.75× faster than the simulator's ray tracer at competitive image quality, while the diffusion-based model reached higher visual fidelity at significantly higher inference cost. This work has been accepted at the European Modeling & Simulation Symposium (EMSS 2026).

The project focuses on developing a methodology for monitoring bearing conditions. It combines Poincare Maps, Fast Fourier Transform (FFT), and Convolutional Neural Networks (CNN) to diagnose faults and predict the Remaining Useful Life (RUL) of bearings. 

For a Research Project course in the Automated Driving department, we are working on "Visual Lane Following for Scaled Automated Vehicles". The vehicle is based on the NVIDIA-backed open-source JetRacer platform, including an NVIDIA Jetson Nano. Specifically, I am working on lane detection using classical and deep learning techniques.

As part of the graduate-level Mechanisms and Robotics course, I led a team of four students to develop an Apple Fruit Picker robot. We modeled the robot using SolidWorks and created animations in Blender. MATLAB and Python were employed to compute the robot's Forward and Inverse Kinematics and Dynamics.

As part of a Design Oriented Project for my Physics degree, I worked on implementing Machine Learning Models for classification of Galaxies in the Galaxy Zoo dataset. Additionally, I implemented a Decision Tree Regressor model to predict the redshift in Galaxies using data from the SLOAN Digital Sky Survey (SDSS) .