I am a researcher with a strong interdisciplinary background in applied mathematics, electrical engineering, and management. I completed my MASt in Applied Mathematics (Part III of the Mathematical Tripos) at the University of Cambridge, where I focused on statistics and probability. Prior to that, I earned a BSc in Electrical Engineering and Information Technology from ETH Zürich, specializing in quantum photonics and control.

Recently, I was working as an ML researcher at Helmholtz AI, where I focus on uncertainty quantification for large language models, cross-modal protein retrieval, and Bayesian inference techniques. My research has been published on platforms like arXiv and combines theoretical insights with practical applications in areas such as foundation models and explainable AI. I also gained industry experience as a data science intern at BASF SE, where I developed predictive maintenance models for chemical production processes.

I am fluent in German and English, proficient in programming languages such as Python, C++, and CUDA, and experienced with machine learning frameworks like PyTorch, TensorFlow, and Huggingface. Beyond my technical work, I have held leadership roles, including serving as a board member of the Engineering Association at ETH and as president of the LSE Bankside House Committee.