Current Projects

Current research projects in recurrence networks, complex systems, biomedical signals and astronomy data

My present research work is centred on developing interpretable computational methods for complex systems. These projects connect nonlinear time series analysis, recurrence networks, ECG and biomedical signals, topological data analysis, radio astronomy, student space-technology activities and AI-native science education.

Recurrence Networks ECG and Biomedical Signals MeerKAT and Radio Astronomy Topological Methods AI-Native Education

Project Vision

My current projects are guided by one central question: how can complex data be converted into meaningful structures that reveal system behaviour? In my work, a time series is not treated only as a sequence of numbers. It is reconstructed, transformed and studied as a recurrence structure, network, topology or interpretable feature space.

This approach is being extended across nonlinear dynamical systems, ECG and cardiac signals, trajectory data, astronomy and radio-astronomy datasets, student space-technology projects and AI-native learning research.

Core direction: from signal to phase space, from phase space to recurrence or network structure, and from structure to scientific interpretation.
Research Themes Publications Contact for Collaboration

Active and Developing Projects

These are the major current project directions connected to my research, publications, workshops and student mentoring.

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Core research

Tracking Dynamical Transitions using Link Density of Recurrence Networks

This project investigates how recurrence-network measures can be used to identify transitions in nonlinear dynamical systems. The focus is on link density and related network descriptors as sensitive indicators of changing dynamics.

Main tasks
  • Construct recurrence networks from reconstructed phase-space trajectories.
  • Use sliding-window link density to track dynamical changes.
  • Test the method on standard nonlinear systems and real-world signals.
  • Interpret transitions using network structure rather than only numerical fitting.
Recurrence Networks Link Density Dynamical Transitions Complex Systems
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Biomedical research

Physiologically Interpretable ECG Classification using Recurrence-Network Topology

This project extends recurrence-network methods to ECG signals. The aim is to extract features that are not only useful for classification but also meaningful in relation to cardiac dynamics.

Main tasks
  • Prepare ECG datasets for recurrence and network-based analysis.
  • Preserve physiologically meaningful amplitude and temporal information.
  • Extract network features such as clustering, path length, heterogeneity and entropy-related descriptors.
  • Combine interpretable features with machine learning classification.
ECG Cardiac Dynamics Biomedical Signals Machine Learning
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Topological direction

Topological Methods for Trajectory Analysis and Complex Data

This direction explores how topological and geometric descriptors can be used to analyse complex datasets, trajectories and planned paths. It connects data shape, persistence, similarity and optimisation-related patterns.

Main tasks
  • Represent trajectories and evolving datasets as geometric or topological structures.
  • Extract shape-based and persistence-inspired descriptors.
  • Compare planned and observed paths using topological signatures.
  • Extend topological ideas to broader complex-data problems.
Topological Data Analysis Trajectory Analysis Persistent Structure Complex Data
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IUCAA-linked direction

MeerKAT and Radio-Astronomy Data Analysis Workflows

This developing direction connects my IUCAA association, astronomy-club activities and the IUCAA-sponsored MeerKAT workshop at RSET. The project explores how radio-astronomy datasets can be approached using scientific computing, imaging tools and data-analysis methods.

Main tasks
  • Develop student-friendly workflows using Python, Astropy, CASA and CARTA.
  • Understand visibilities, imaging, source structure and radio-astronomy data products.
  • Explore how recurrence-network and complex-network methods may be connected to source variability.
  • Build a bridge between engineering students and modern radio-astronomy data analysis.
MeerKAT CASA CARTA Astropy Radio Astronomy
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Astronomy data

Nonlinear Analysis of Astrophysical Light Curves and Source Variability

This project direction applies nonlinear time series and recurrence-based methods to astronomical variability. Possible data sources include black-hole accretion systems, compact sources, variable stars, X-ray binaries and public astronomical time-series datasets.

Main tasks
  • Collect or use public light-curve datasets from astronomical sources.
  • Apply recurrence plots, recurrence networks and entropy-related measures.
  • Study variability, state changes and complex temporal structure.
  • Develop student projects connecting astronomy and nonlinear dynamics.
Light Curves Black Hole Accretion Source Variability Astrophysics
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Student space projects

RSET-SAT, SDR Ground Station and Space-Technology Learning Projects

This project direction supports student learning in satellite technology, SDR-based reception, antenna tracking, NOAA satellite signal reception, CubeSat concepts and space-technology applications.

Main tasks
  • Introduce students to satellite communication and ground-station concepts.
  • Use RTL-SDR and related tools for signal reception and analysis.
  • Connect CubeSat mission ideas with physics, electronics and data analysis.
  • Build practical student projects through the Astronomy Club and space-technology activities.
RSET-SAT RTL-SDR Ground Station CubeSat Student Projects
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Student-ready

Computational Physics and Network-Based Data Analysis Projects

This is a student-focused project stream that introduces engineering students to physics-based computing, time-series analysis, scientific visualisation, recurrence plots, networks and machine learning.

Main tasks
  • Train students in Python, MATLAB and Jupyter-based scientific workflows.
  • Use simulated systems and real datasets for analysis.
  • Develop small research modules that can grow into papers or conference presentations.
  • Connect classroom physics with data-driven research practice.
Python MATLAB Jupyter Network Science Student Mentoring
AI
Educational research

AI-Native Physics Learning and Entrepreneurial Science Education

This project direction studies how artificial intelligence changes physics learning, scientific thinking, teacher training, student identity formation and the movement from self-actualisation to world-actualisation.

Main tasks
  • Develop AI-native physics learning activities for teachers and students.
  • Connect natural intelligence, artificial intelligence and scientific reasoning.
  • Design workshop models where participants create socially meaningful learning outputs.
  • Build a framework for AI-native and entrepreneurial education in physics.
AI-Native Learning Physics Education Entrepreneurial Education Teacher Training

Common Research Workflow

Most of my projects follow a common pathway from data to interpretable scientific meaning.

1 Data Collect or simulate time series, ECG signals, light curves, SDR data, trajectories or learning data.
2 Representation Construct phase-space, recurrence, network, topological or feature-based representations.
3 Measures Extract link density, clustering, path length, entropy, topology and machine-learning features.
4 Interpretation Connect features with physical, biological, astronomical, engineering or educational meaning.
5 Output Prepare papers, student projects, workshops, tools, presentations and collaborative proposals.

Student Project Opportunities

Students can begin with small, well-defined tasks and gradually move toward research-level projects.

Beginner Level Python basics, plotting, simulated signals, recurrence plots and simple scientific visualisation.
Intermediate Level Network construction, ECG analysis, astronomy light curves, SDR data and feature extraction.
Advanced Level Machine learning, recurrence-network topology, transition detection, topological descriptors and paper preparation.

“The purpose of these projects is not only to analyse data, but to build meaningful structures through which complex systems can be understood.”

For Students

Students interested in computational physics, nonlinear dynamics, ECG analysis, astronomy data, SDR/space-technology projects, machine learning or AI-native learning can approach for project-based research.

Expected qualities: curiosity, willingness to learn coding, patience with data, and interest in physics-based interpretation.
Student Research Projects

For Collaboration

I welcome collaborations involving nonlinear time series, recurrence networks, biomedical signals, radio astronomy data, topological methods, space-technology education and AI-native science education.

Contact for Collaboration View Publications