I am a Ph.D. student at TUM's Computer Vision Group, headed by Prof. Dr. Daniel Cremers. My current research focuses on improving monocular 3D reconstruction and localization accuracy and efficiency.

Before joining the Ph.D. program, I worked as a Machine Learning Engineer at Knowlix GmbH. There, I researched OCR modeling and LLMs such as GPTs and Llama. I also designed data and machine learning pipelines and applied machine learning techniques to solve real-world problems.

I completed my Master's studies in Electrical Engineering and Information Technology at TUM. My focus was on Automation, Robotics, and AI. I spent a semester abroad at NTU Singapore, where I studied Deep Learning for Natural Language Processing and Genetic Algorithms.

For my Master's thesis, I worked at BMW Group AG on AR glasses and head 6DoF pose estimation based on 3D multi-view geometry reconstruction and deep learning. My research involved implementing a system that could estimate the head and AR glasses pose with high accuracy (Project). It resulted in a publication titled "Comparing Head and AR Glasses Pose Estimation."

I also completed my Bachelor of Science in Electrical Engineering and Information Technology at TUM, with a focus on Signal Processing. During my Bachelor's thesis, I worked on generative modeling using Capsule Generative Adversarial Networks at the Chair of Robotics, Artificial Intelligence, and Real-time Systems at TUM.

• Unsupervised Learning for 3D Reconstruction
• Large-Scale Scene Reconstruction
• Cross-View Reconstruction
• Relocalization in Changing Environments

I am looking for highly motivated and talented Master's students passionate about Computer Vision and Machine Learning. Our research will focus on fascinating topics in collaboration with DeepScenario, including monocular 3D reconstruction and camera localization, to contribute cutting-edge research to top-tier conferences like CVPR, ICCV, and ECCV.

If you are interested, please email me. Please attach your CV and a recent transcript. Having a strong mathematical background, proficiency in Python, and experience working with PyTorch/Tensorflow will be considered.