Astronomical scale
Topological descriptors for galaxy morphology, asteroseismology, and gravitational-wave signals, using tools such as persistent homology, Mapper, and graph-based representations.
Physics · Topological Data Analysis · Machine Learning
Gabriel Wendell Celestino Rocha
Physicist and PhD student at UFRN (Brazil) working on Topological Data Analysis and Topological Deep Learning applied to physical systems across scales – from magnetic materials and dynamical systems to galaxies, gravitational waves, and asteroseismology.
Research overview
My work focuses on how topological and geometric methods can uncover structure in complex physical data, and how these structures can inform modeling, prediction, and inversion problems in physics.
Mesoscopic scale
Topology in magnetohydrodynamics, fluid instabilities, and dynamical systems, exploring how homological features relate to transport, mixing, and phase-space structure.
Microscopic scale
Topological analysis of Barkhausen noise, disordered media, and quantum information, linking topological signatures with domain-wall dynamics and entanglement structure.
News & highlights
A few recent activities and projects. For more details, see the publications, talks, and software pages.
- [2025] Developing a PhD project on Topological Data Analysis and Topological Deep Learning Applied to Physical Systems Across Scales.
- [2025] Project on TDA-based detection of gravitational-wave-like signals at low SNR for the 4th School on Data Science & Machine Learning.
- [2025] TDA diagnostics of CNN predictors applied to shock tube dynamics, with results presented at the XXXIX EFNNE meeting.
Contact
The best way to reach me is by email. I am always open to discussions and collaborations involving TDA/TML, astrophysics, complex systems, and scientific computing.