Data analysis and signal processing provide the tools that scientists can use to investigate and understand the physical world. However, these tools contain assumptions and/or limitations that can make the interpretation of the data difficult or lead to false conclusions. Therefore, theoretical focus of our research group is around developing and extending the application of powerful tools of mathematical statistics (e.g., statistical decomposition, blind source separation, and Bayesian techniques) in the Earth Science research. Besides, by extending the availability of satellite geodetic and Earth observation (EO) data, we explore new algorithms to use the large pool of available data for understanding physical processes in the form of data driven techniques such as artificial intelligence, neural network and deep learning algorithms.
Statistical Signal Decomposition
Bayesian Techniques
Functional Modelling
Artificial Intelligence
