Hub Aligned Community
Our Hub Aligned Community brings together students and postdoctoral researchers whose work closely aligns with the core research areas of the Erlangen AI Hub.
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.
Dr Daniel Platt
Imperial College London
Daniel is a research fellow at Imperial College London working on problems at the intersection of AI and geometry. He is interested in using AI to answer questions in differential geometry, in particular using approximate solutions to partial differential equations in geometric analysis.
Dr Vukašin Stojisavljević
University of Oxford
Vukašin is a Postdoctoral Research Associate at the Mathematical Insititute at the University of Oxford. His research focuses on theoretical aspects of topological data analysis and its interactions with various areas of geometry, analysis and dynamics.
Dr Sara Veneziale
Imperial College London
Sara is a Chapman-Schmidt Research Fellow at the I-X Centre for AI in Science and the Department of Mathematics at Imperial College London. Her research focuses on using AI to discover and prove new results in mathematics, and on the high-dimensional geometry that underpins Large Language Models. In parallel to her research, she has co-designed and co-delivered training courses in the fundamentals of AI to 100+ civil servants from 10+ government departments.
Dr Lukas Waas
University of Oxford
Lukas Waas is a Postdoctoral Research Associate at the University of Oxford. His research spans homotopy theory, stratified spaces, and topological data analysis. In particular, he develops stratified persistent methods for studying heterogeneous and singular data, providing mathematically robust tools for analysing and detecting complex geometric and topological structures.
Dr Casey Garner
University of Oxford
Dr Casey Garner is a National Science Foundation postdoctoral researcher from the United States. He completed his doctoral studies in mathematics at the University of Minnesota under the supervision of Profs Gilad Lerman and Shuzhong Zhang. He is currently researching constrained nonconvex optimization under the mentorship of Prof Coralia Cartis at the Mathematical Institute, University of Oxford.
Dr Leoni Wirth
University of Oxford
Leoni Wirth is a Postdoctoral Research Associate in the Department of Statistics at Oxford University. Her research focuses on the study of spatial random graphs at the intersection between probability theory and statistics, using Stein’s method, kernelized Stein discrepancies and tools from spatial stochastics. She received her PhD in mathematics from the Georg-August University Göttingen.
Dr Alessandro Micheli
Imperial College London
Alessandro is a Postdoctoral Research Associate at Imperial College London. His research focuses on probabilistic AI and geometric machine learning, particularly at their intersection, with an emphasis on tackling real-world scientific challenges. He earned a PhD in Mathematics from Imperial College London.