I am a German national from Hannover and a PhD student in the ELPIS group, with a solid background in engineering and applied mathematics. My academic journey and professional experiences have equipped me with a deep understanding of technical principles and a passion for innovative solutions. I am particularly interested in applying Bayesian principles, especially uncertainty quantification, to large-scale AI systems, with a focus on transformer architectures and diffusion models.

My research interests are rooted in the potential of AI to transform industries and improve lives. I am deeply fascinated by the healthcare sector and protein analysis space, where I believe AI can make significant breakthroughs in understanding complex biological processes and developing new treatments. Additionally, my work explores the intricacies of natural language processing (NLP) and the challenges of quantifying uncertainties in large language models (LLMs). These areas are crucial for enhancing the reliability and interpretability of AI systems in real-world applications.

Most of my applications are in the deep generative AI space, where I aim to push the boundaries of what is possible with current technologies. By leveraging my expertise in Bayesian methods and my keen interest in transformer architectures and diffusion models, I strive to contribute to the development of AI models that are not only powerful but also trustworthy and transparent. Through my research, I hope to address some of the most pressing challenges in AI, particularly in healthcare and NLP, and to pave the way for new discoveries that can benefit society at large.