Martina Jørgensen

experienced project graduate (mathematician, data scientist)

I recently joined the NGT team at CERN after completing my PhD in pure mathematics at ETH Zürich, where I worked under the supervision of Prof. Urs Lang. My doctoral research focused on differential and metric geometry, with particular emphasis on notions pertaining to hyperbolicity and non-positively curved spaces. After several years working in pure mathematics, I am enthusiastic about applying my theoretical background to the applied research environment of particle physics, a field that was a significant part of my BSc studies.

My current research interests lies in the application of deep learning methods in high-energy physics, specifically the development and optimisation of transformer models for jet tagging. Within NGT, my work involves the mathematical optimisation of compression techniques and the exploration of emerging approaches like tensor networks, to enhance the efficiency and performance of physics-informed machine learning models.

Fields of interest:

• Deep Learning in High-Energy Physics
• Mathematics (Differential and Metric Geometry)
• Transformer Models and Jet Tagging
• Tensor Networks
• Model Compression and Mathematical optimisation