My general research interest lies in applying and developing numerical techniques to access and describe quantum physical phenomena from fundamental principles. As part of my PhD project under the supervision of Dr George Booth, I am exploring novel approaches to efficiently describe and approximate many-body quantum states for the description of physical phenomena otherwise inaccessible by exact methods. In particular, we exploit connections to standard tasks within the field of Machine Learning to design explicitly data-driven representations emerging from physical intuition and rigorous statistical principles. We aim to develop a universal understanding how particular types of (quantum) correlations can be compressed most efficiently, with the overall goal to develop a general numerical toolbox for a variety of quantum and classical many-body problems, ranging from the electronic structure of molecules and materials to generic Machine Learning tasks.
A list of my publications can be found via my ORCiD page.