Research Tools & Codes
My research uses scientific computing to move from raw data to interpretable structure. The tools listed here support nonlinear time series analysis, recurrence networks, ECG analysis, topological methods, astronomy workflows and student research mentoring.
This page records the computational tools and code workflows that are genuinely connected to my research, teaching and student-project mentoring. The focus is not on listing many software names, but on showing how each tool helps in moving from data to interpretation.
These four directions summarise how the tools are used in my research and mentoring.
Signals, phase-space reconstruction, recurrence plots and hidden dynamical structure.
ECG signals, recurrence-network features and physiologically interpretable classification.
FITS files, MeerKAT learning workflows, CASA, CARTA, Astropy and radio-astronomy data exploration.
Python, MATLAB and Jupyter notebooks used as research, teaching and student-project environments.
These tools support my nonlinear-dynamics, network-science, biomedical, astronomy and education-related work.
Used for numerical analysis, signal processing, recurrence-network construction, machine learning, plotting and astronomy data workflows.
Used for nonlinear time-series experiments, recurrence plots, simulations, signal processing and quick prototyping.
Used as a reproducible environment where code, explanation, visualisation and interpretation stay together.
Used to reconstruct hidden dynamics, build recurrence structures and extract features such as link density, clustering and path length.
Used for ECG preprocessing, segment preparation, recurrence-network features and interpretable classification.
Used to combine interpretable features with classification workflows, especially in biomedical and complex-system datasets.
Used for astronomy-related Python workflows, FITS files, coordinates and scientific data analysis.
Used for radio-astronomy data analysis, visualisation and learning workflows related to MeerKAT and interferometric imaging.
Used for student learning in satellite reception, radio signals, antenna systems and space-technology activities.
These are the code directions connected with my ongoing work and student mentoring.
A workflow for converting time series into recurrence plots and recurrence networks, followed by graph-based feature extraction.
Typical componentsA sliding-window analysis pipeline for tracking changes in nonlinear systems using recurrence-network measures.
Typical componentsA biomedical signal workflow for ECG preprocessing, recurrence-network features and classification support.
Typical componentsA student-friendly workflow for learning astronomical data analysis using Python, Astropy, CASA and CARTA.
Typical componentsA workflow for representing trajectories and complex datasets through geometric and topology-inspired descriptors.
Typical componentsA teaching and workshop design workflow for physics learning in the age of artificial intelligence.
Typical componentsI prefer a workflow where every result can be traced from data to code to interpretation.
Selected research scripts, teaching notebooks and workflows may be shared with students or collaborators depending on the project status, publication stage and academic context.
Public repositories may be added later after cleaning the scripts, adding documentation, example datasets, usage instructions and citation details.
“In my research and mentoring, code is not only a technical tool. It is a bridge between mathematical ideas, physical systems, data-driven discovery and student learning.”
Students interested in computational physics, ECG analysis, recurrence plots, astronomy data, SDR experiments, Python, MATLAB or machine learning can begin with simple modules and gradually move toward research-level projects.
I welcome collaborations where computational methods, recurrence networks, biomedical signals, astronomy data, topological descriptors or AI-native learning frameworks can be developed and applied.