Hub PHD Students
The Erlangen AI Hub currently supports eight PhD students working across its research themes.
PhD students
Ugur Canturk
University of Southampton
Ugur is a PhD student at the University of Southampton under the supervision of Dr Ruben Sánchez-García and Prof Jacek Brodzki. His research explores approximate symmetries in graphs and the use of doubly stochastic matrices in symmetries from the perspective of mathematical foundations of AI. He is also working on integrating topological data analysis into deep learning models.
Xavier Crean
Swansea University
Xavier is a PhD student at Swansea University working in collaboration with Prof Jeffrey Giansiracusa and Prof Biagio Lucini (Queen Mary University of London). His research involves
developing data-driven topological feature extraction methods to study deconfinement phase transitions in computational particle physics.
developing data-driven topological feature extraction methods to study deconfinement phase transitions in computational particle physics.
David Lanners
Durham University
David is a PhD student at Durham University, supervised by Prof Jeffrey Giansiracusa and Dr Tin Sulejmanpasic. His research focuses on creating an efficient pipeline to track topological features in computer vision using zigzag persistence, a method from topological data analysis (TDA). He is also helping to build the foundation of a novel computational framework for differential geometry called diffusion geometry.
Matilde Muzzolini
Queen Mary University of London
Matilde is a PhD student at Queen Mary University of London under the supervision of Professor Omer Bobrowski. Her research focuses on applying methods from topological data analysis to the study of deep neural networks, with a particular interest in weight space learning.
Arne Wolf
Imperial College London
Arne is a PhD student at the London School of Geometry and Number Theory and at Imperial College London under the supervision of Dr Anthea Monod. Arne uses several methods from geometry to investigate simplicial complexes and their generalizations. He is particularly interested in cellular sheaves, persistent homology and all kinds of Hodge Laplacians.
Flavio Gualtieri
Imperial College London
Flavio Gualtieri is a PhD student working on applications of Topological Data Analysis in deep learning. He holds a B.S. in Mathematics and a B.A. in Physics from the University of Chicago, and an M.S. in Theoretical Physics from King’s College London. His master’s thesis explored Topological Quantum Error Correction. After working as a Data Scientist at PwC, he joined Omer Bobrowski’s research group.
Michal Kozyra
University of Oxford
Michal Kozyra is a DPhil student in Machine Learning at the University of Oxford. His research focuses on the foundations of deep learning, with particular interests in generalisation, robustness, and out-of-distribution behaviour. He develops theory-informed methods connecting probabilistic tools, geometry, and modern neural architectures, including applications of Stein’s method and singular learning theory. Prior to his doctorate, he worked as a research scientist in industry, designing and analysing machine-learning models in low signal-to-noise settings.
Yulin Song
Imperial College London
Yulin Song is a PhD student at Imperial College London under the supervision of Professor Anthea Monod. His research lies at the interface of mathematics and AI, with a particular focus on applications of geometric group theory. He is interested in how ideas from geometric group theory can inform and enhance methods in machine learning and topological data analysis. He is also keen on AI-assisted theorem proving, including the use of reinforcement learning and related approaches to support research on problems in geometric group theory, such as the isomorphism problem.