Type-II Neural Symmetry Detection with Lie Theory
This work is driven by the results in my previous paper on LLMs. Note Create your slides in Markdown - click the Slides button to check out the example.
Professional Summary
Alex Gabel is an Industrial AI Engineer at Paro and a PhD Candidate at the University of Amsterdam, where he is currently investigating symmetry detection and structural bias learning. His research has been published in journals such as Scientific Reports and IOP’s Machine Learning: Science and Technology, including various workshops at top AI conferences. Alex is a problem-solver at heart, whether it be in the realm of AI, theoretical physics, or at the boulder hall. When not working on research, Alex enjoys watching an art-house film or reading a good book, especially one about philosophy.
Education
PhD Artificial Intelligence
University of Amsterdam
MSc Artificial Intelligence
Imperial College London
MSc Film Studies and Visual Culture
University of Antwerp
MMathPhys Mathematical and Theoretical Physics
University of Oxford
Interests
Geometric Deep Learning
Dynamical Systems
PDEs
Theoretical Physics
Differential Geometry
Philosophy
📚 My Research
I’m an Industrial AI Engineer by day and Research Scientist by night, channeling my love for mathematics, physics, and problem-solving into new insights and concrete solutions.
My modus operandi ranges from experimenting with coding up deep learning architectures to solving problems using pen-and-paper, with experience in both industry and academia.
Please do reach out if you would like to chat!
Featured Publications
Data-driven Lie Point Symmetry Detection for Continuous Dynamical Systems
This work is driven by the results in my previous paper on LLMs. Note Create your slides in Markdown - click the Slides button to check out the example.
Recent Publications
(2025).
Type-II Neural Symmetry Detection with Lie Theory.
Scientific Reports.
(2024).
Data-driven Lie Point Symmetry Detection for Continuous Dynamical Systems.
ML: Sci. and Tech..
(2023).
Learning Lie Group Symmetry Transformations with Neural Networks.
Proceedings of 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML).
Recent & Upcoming Talks
Example Talk
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