Machine Learning

This page contains all content related to Machine Learning research, including applications in reservoir computing, network analysis, and bio-inspired systems.

Key Research Areas

  • Reservoir Computing: Developing efficient machine learning approaches using dynamical systems
  • Network-based ML: Machine learning on complex networks and graph structures
  • Bio-inspired Learning: Learning algorithms inspired by biological information processing

Browse the content below to explore publications, projects, and collaborations in machine learning.

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Reservoir Computing for image classification

Study on reservoir computing methods applied to image classification.

Mehdi Ghorbani
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Research Featured in AIP Scilight: Optimizing Reservoir Computing for Noisy Nonlinear Systems featured image

Research Featured in AIP Scilight: Optimizing Reservoir Computing for Noisy Nonlinear Systems

Research on optimizing reservoir computing for studying noisy nonlinear systems has been featured in AIP Scilight, highlighting innovative approaches to signal processing and …

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Reservoir Computing Conference 2026 (RCC26), TU Berlin featured image

Reservoir Computing Conference 2026 (RCC26), TU Berlin

The Reservoir Computing Conference 2026 (RCC26) is an international conference dedicated to the field of reservoir computing and related machine learning approaches. Co-organizing …

DFG SPP 2353 Jahrestreffen 2025, Wiesbaden

Talk on "Reservoir Computing as Design Assistants Under Limited Data" at the DFG SPP 2353 Jahrestreffen 2025.

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Dr. Manish Yadav
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Preprint
Network structure-function relationship | New Preprint featured image

Network structure-function relationship | New Preprint

This project has been started to understand emergent structure-function relationship of evolving networks. Part-1: Performance-Dependent Network Evolution (PDNE) framework. I …

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Evolution beats random chance: Performance-dependent network evolution for enhanced computational capacity featured image

Evolution beats random chance: Performance-dependent network evolution for enhanced computational capacity

Can evolution beat random chances? Our answer: Yes, we have a proof! 😃

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Dr. Manish Yadav
Predicting multi-parametric dynamics of an externally forced oscillator using reservoir computing and minimal data featured image

Predicting multi-parametric dynamics of an externally forced oscillator using reservoir computing and minimal data

This study uses a data-driven approach to investigate how bifurcations can be learned from a few system response measurements.

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Dr. Manish Yadav
DFG SPP 2353 Jahrestreffen 2024, Bergisch Gladbach featured image

DFG SPP 2353 Jahrestreffen 2024, Bergisch Gladbach

Talk presented at the DFG SPP 2353 Annual Meeting in Bergisch Gladbach 2024.

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Dr. Manish Yadav
DFG SPP Doktorandtreffen 2024, Mainz featured image

DFG SPP Doktorandtreffen 2024, Mainz

Talk on Reservoir Computing as design assistants under limited data at the DFG SPP 2353 Doktorandtreffen 2024.

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Dr. Manish Yadav

Data Driven Dynamics (D3) Community 2024

Talk on Reservoir Computing for time-Series prediction presented at the Data Driven Dynamics (D3) Community meeting.

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Dr. Manish Yadav