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Curriculum vitae · updated August 2026
Physics graduate · computational physicist
Physics graduate and early-career computational physicist working on dense-matter equations of state, relativistic stellar structure, and reproducible scientific software. Focused on connecting explicit physical models to compact-star behaviour through numerical and statistical methods, with particular attention to validation, provenance, and scientific scope.
Continued post-BSc research collaboration and guidance with Charalampos Moustakidis and Theodoros Diakonidis; scientific interpretation and responsibility retained by Ioannis Papathanasiou.
Developed a fail-closed workflow for smooth sound-speed modifications, effective cold-barotrope reconstruction, and TOV/tidal response calculations. Released versioned software and checksum-verified campaign evidence; sole-authored public arXiv preprint arXiv:2608.23033v1.
Built a synthetic equation-of-state, stellar-structure, and machine-learning workflow to study restricted compact-star model discrimination. A subsequent audit narrowed the defensible classification scope and documented leakage, provenance, and generalization risks.
Audited and redeveloped the restricted synthetic classification workflow, with physical-family partitioning and explicit interpretation limits. Developed separate baseline and perturbation-sensitivity demonstrations whose outputs are labelled as uncalibrated model scores rather than astrophysical probabilities.
Submitted 2026-08-24; cross-listed in astro-ph.HE. Public arXiv preprint, not peer reviewed or journal published; no journal submission is planned.
Developer and maintainer · Python · MIT licensed.
Audited alpha development work · Python · no formal release.
Baseline demonstration ·Perturbation sensitivity · restricted synthetic model scores, not observational inference.
Undergraduate thesis: Machine Learning Classification of Neutron Star Composition(10/10). Official examiners: Charalampos Moustakidis, Theodoros Diakonidis, and Theodoros Gaitanos.
Equation-of-state modelling; TOV and tidal calculations; numerical ODE integration; supervised learning with grouped validation; reproducible workflows and provenance.
Python, NumPy, SciPy, pandas, scikit-learn, Jupyter, Git, and LaTeX.
Customer-facing production and order coordination; developed small Python utilities for batch printing and label generation.