Physicist & PhD student @ UFRN · Topological Data Analysis & Machine Learning in Physics · Scientific Computing & Simulations

$$ \partial_{u}\left[\Lambda^{u}-\frac{\partial\mathcal{L}}{\partial\left(\partial_{u}\Phi\right)}\delta\Phi=0\right] $$

I am a Physics PhD student working on Topological Data Analysis (TDA) and Machine Learning applied to complex physical systems.
Most of my research lives at the intersection of:

• Astronomical scale

  • Gravitational-wave detection at low SNR
  • Asteroseismology and stellar structure
  • Galaxy morphology and large-scale structure

• Mesoscopic scale

  • Magnetohydrodynamics (MHD) & fluid dynamics
  • Nonlinear instabilities (Kelvin–Helmholtz, Rayleigh–Taylor)
  • Low-dimensional chaotic systems (Lorenz, Rössler, Chua, etc.)

• Microscopic scale

  • Magnetic materials & Barkhausen noise
  • Condensed matter systems and phase transitions
  • Quantum information and entanglement structure

Across these domains, I like to ask:

What can topology “see” in data that classical statistics cannot?

These are some repositories that capture what I enjoy building: rigorous, research-grade, and reproducible workflows.

You can find more projects in my repositories. Many of them are part of a broader effort to build reusable tools for scientific computing in physics.

Some of my scientific work (see my ORCID/ResearchGate for a complete list):

• Computational Modeling of a Star: A Theoretical and Numerical Approach – Revista Brasileira de Ensino de Física
• Generalization to d-dimensions of a fermionic path integral for exact enumeration of polygons on hypercubic lattices – Scientific Reports (Nature)
• The limb darkening via transit modeling and Bayesian inference – Revista Brasileira de Ensino de Física

I am particularly interested in papers and projects that connect mathematical structures (topology, geometry, stochastic processes) with observable physical phenomena.

I enjoy teaching as much as research, and often design materials that are both mathematically rigorous and code-driven:

• PET.py notebooks (PET–Física / UFRN)
  • Extensive collection of notebooks on astronomy, fluids, quantum mechanics, and dynamical systems.
• Minicourse – Uma breve introdução ao Machine Learning (PET–Física / UFRN)
  • 5-day course covering supervised & unsupervised learning, tensor computation, and dimensionality reduction.
• Minicourse – Introdução ao Python para Engenharia Química (UFRN)
  • Hands-on workshop on Python for scientific and engineering applications.

Whenever possible, I publish the materials here on GitHub so they can be reused, remixed, and improved.

I work mainly in the Python scientific ecosystem, with a focus on tools that scale to real research workloads:

• Languages

  • Python, a bit of C/C++ when needed for performance, Markdown/LaTeX for documentation.

• Numerical & scientific computing

  • NumPy, SciPy, pandas, SciPy stack for ODE/PDE solving, optimization, and signal processing.

• Machine learning & deep learning

  • scikit-learn, TensorFlow/Keras, classical ML pipelines, and experiment workflows with explicit configs and reproducible seeds.

• Data & visualization

  • Matplotlib, seaborn, Plotly/Dash/Streamlit for interactive dashboards, Jupyter/nbconvert for reproducible reports.

• Dev & tooling

  • Git/GitHub, Vim/Neovim, VS Code, Linux environments, and simple automation for pipelines.

I care a lot about clean project structure, version control, and reproducibility (configs, seeds, environment files, and clear documentation).

• 🌌 I like to think about physics “across scales” – from quantum scales to astronomical scales.
• 🌺 A PhD student working with TDA & ML applied to physics and who loves flowers.
• 💬 I’m always happy to discuss physics, topology, ML, and research workflows – feel free to open an issue, start a discussion, or reach out.

Thanks for visiting! If you find something useful here, consider starring a repository or saying hi. 😊