Research Overview

Understanding complex systems through nonlinear dynamics, networks and data-driven physics

My research explores how hidden patterns, transitions and structures can be extracted from complex data. The work connects nonlinear time series analysis, recurrence networks, complex networks, biomedical signals, topological methods, astrophysical data and AI-native science education.

Nonlinear Time Series Recurrence Networks Complex Systems Biomedical Signals Astrophysical Data

Research Identity

I work at the intersection of physics, nonlinear dynamics, network science and data analysis. My central research interest is to transform observed data into meaningful mathematical and computational representations that reveal the behaviour of complex systems.

The foundation of my research lies in nonlinear time series analysis and complex networks. Time-dependent signals from nature, biological systems, engineering systems and astronomical sources often contain signatures of transitions, irregularity, hidden order and system-level organisation. My work attempts to identify these signatures using recurrence plots, recurrence networks, network measures, topological descriptors and machine-learning-supported analysis.

Central idea: A time series is not only a sequence of numbers. It can be reconstructed as a phase-space object, converted into a network or topological structure, and then studied to understand the underlying dynamics.
View Publications Research Themes Contact for Collaboration

Major Research Themes

My research themes are connected by one broad question: how can complex data be transformed into interpretable structures that reveal dynamics, transitions and system behaviour?

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Nonlinear Time Series Analysis

Analysis of signals from nonlinear and chaotic systems using phase-space reconstruction, recurrence analysis and dynamical measures.

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Recurrence Networks

Conversion of recurrence structures into complex networks to study hidden geometry, transitions and dynamical organisation.

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Complex Networks

Use of network measures such as link density, clustering, path length and degree-based descriptors to quantify complex behaviour.

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Biomedical Signal Analysis

Application of recurrence-network and topology-inspired methods for ECG analysis, cardiac dynamics and physiologically interpretable classification.

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Astrophysical Data Analysis

Exploration of complex variability in astronomical and radio-astronomical data using nonlinear, network and computational methods.

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Topological Data Analysis

Use of topological and geometric descriptors for analysing complex datasets, trajectories, patterns and evolving structures.

AI

AI-Native Science Education

Research on AI-native learning, scientific thinking, world-actualisation, and the transformation of physics education in the age of artificial intelligence.

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Computational Physics

Development and use of computational workflows in Python, MATLAB, Jupyter, machine learning, graph analysis and scientific visualisation.

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Interdisciplinary Complex Systems

Extension of nonlinear and network-based methods to physical, biological, astronomical, engineering and educational systems.

Research Methodology

The methodological pathway begins with observed data and moves toward interpretable scientific insight.

1 Data Signals, time series, images, trajectories or experimental observations.
2 Reconstruction Phase-space reconstruction, embedding and representation of hidden dynamics.
3 Network / Topology Conversion into recurrence networks, graphs or topological structures.
4 Measures Extraction of link density, clustering, path length, entropy and structural descriptors.
5 Insight Detection of transitions, classification, interpretation and scientific understanding.

Current Research Directions

My current work focuses on strengthening the connection between dynamical systems theory, network science, topological methods and real-world data.

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Tracking dynamical transitions Using link density and recurrence-network measures to detect changes in nonlinear systems.
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Physiologically interpretable ECG analysis Developing recurrence-network and topology-based methods for biomedical signal classification.
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Radio astronomy and astrophysical data Exploring nonlinear and network-based methods for complex astronomical datasets.
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Topological and network descriptors Applying geometric, topological and graph-based measures to complex data analysis.

Tools, Skills and Platforms

The research work combines theoretical understanding, computational modelling and data-driven analysis.

Python MATLAB Jupyter Notebook Recurrence Plots Recurrence Networks Complex Networks Machine Learning Signal Processing Topological Data Analysis Astropy CASA CARTA Scientific Visualisation
Research aim: to develop interpretable computational methods that can move from raw data to scientific meaning.

“My research interest is to understand how complex systems reveal their hidden structure through time, recurrence, networks and topology.”

— Dr. Rinku Jacob

For Students

Students interested in computational physics, nonlinear dynamics, data analysis, biomedical signals, astronomy data or AI-native learning can explore project-based research under these themes.

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Possible project areas ECG analysis, recurrence plots, complex networks, astronomy datasets, machine learning and visualisation.
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Expected skills Curiosity, basic physics understanding, willingness to learn Python or MATLAB, and interest in data analysis.
Student Research Projects

For Collaboration

I welcome academic and interdisciplinary collaborations in nonlinear time series analysis, recurrence networks, biomedical signal analysis, astrophysical data analysis, topological methods, and AI-native science education.

Researchers, students and institutions interested in collaborative research, invited talks, workshops or student mentoring may contact me through the contact page.
Contact for Collaboration View Publications