Overview
Big Data Cybernetics combines principles from cybernetics (systems theory, control, and feedback loops) with big data analytics to understand and manage complex systems. This interdisciplinary field focuses on extracting meaningful insights from large-scale data to enable intelligent decision-making and adaptive control in dynamic environments.
As Professor of Big Data Cybernetics at NTNU, I lead the establishment and development of this emerging field, focusing on applications in autonomous systems, energy, and industrial processes.
Research Topics
Data-Driven Control Systems
Control strategies that learn directly from operational data.
Feedback Loop Optimization
Optimizing control loops using big data analytics.
Complex Systems Modeling
Modeling systems with many interacting components.
Real-time Analytics
Processing and analyzing streaming data for decision support.
Sensor Fusion
Combining data from multiple sensors for improved state estimation.
Multivariate Analysis
Statistical methods for high-dimensional data analysis.