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Tampere UniversityTampere University of Applied Sciences

Subhajit Chatterjee

Marie Curie Research Fellow
Faculty of Engineering and Natural Sciences | Automation Technology and Mechanical Engineering
Tampere University
subhajit.chatterjee [at] tuni.fi (subhajit[dot]chatterjee[at]tuni[dot]fi)
+358504668830
Hervanta Campus
https://orcid.org/0000-0003-1648-6095

About me

I am a Postdoctoral Research Fellow in Machine Learning and Renewable Energy Systems. I am also a member of the Dependability and Automation Research in Cyber-Physical Systems (DARES) Group, which is part of the Dependable Systems Cyber Laboratories. My research disciplines on Artificial Intelligence on critical aspects of federated learning, condition monitoring.

Responsibilities

I specialise in applying advanced machine learning and federated learning to secure and optimise renewable-energy infrastructure. As wind-energy deployment scales globally, conventional isolated data models struggle with limited training data and strict data-privacy constraints. My research addresses this by developing collaborative, distributed AI frameworks for wind-turbine condition monitoring. By enabling turbines and entire wind farms to learn collaboratively without sharing raw operational data, my work improves fault detection and diagnostic accuracy, reduces unplanned downtime, and supports the transition toward smart, sustainable energy systems. A core methodological pillar is the application of federated learning, where I develop privacy-preserving, distributed frameworks that allow multiple entities to jointly train models — significantly improving predictive performance and forecasting accuracy while keeping proprietary data on-site.

Fields of expertise

  • Machine Learning
  • Condition Monitoring
  • Anomaly Detection
  • Federated Learning
  • Signal Processing

Research topics

  • Machine Learning
  • Condition Monitoring
  • Signal Processing
  • Anomaly Detection
  • Predictive Modeling

Latest publications

All publications