Background and approach
The physics comes first.
Computation makes a physical question testable; it should not obscure what the model means or what the result can support.
I am most motivated by problems where fundamental physics, astrophysical systems, and computation meet. Compact objects and dense matter are especially compelling because assumptions made at microscopic scales can influence macroscopic stellar structure and tidal response.
My approach is physics-first: understand the governing model and its domain, then choose the numerical, computational, or statistical tools the question requires. I am interested in machine learning when it can reveal physical structure or distinguish clearly defined scenarios—not as an end in itself.
I value careful modelling, reproducibility, validation, and understanding why a method works. That perspective has been shaped by both my current BSk24 work and the later audit of my undergraduate classifier: in each case, the useful conclusion depends on a clear account of what the model can and cannot establish.
My post-BSc research continues with collaboration and guidance from Charalampos Moustakidis and Theodoros Diakonidis. I retain responsibility for the scientific interpretation and public claims presented here.
- Education
- Degree in Physics, Aristotle University of Thessaloniki, 2026
- Degree record
- 240 ECTS · 8.46/10 (“Very Good”)
- Thesis
- Machine Learning Classification of Neutron Star Composition · 10/10
- Post-BSc guidance
- Continued collaboration and guidance with Charalampos Moustakidis and Theodoros Diakonidis
- Thesis examination
- Charalampos Moustakidis, Theodoros Diakonidis, and Theodoros Gaitanos
- Location
- Thessaloniki, Greece
- Contact
- giannis.papathanasiou.pth@gmail.com
Away from research, I enjoy Formula 1 and aerodynamics, travel, meeting new people, and exploring places on foot.