The Structured and Stochastic Modeling Group is conducting research in statistical signal processing and data analysis, focusing on fundamental questions on how we should model and describe data with random characteristics.
Almost all data encountered in practice has random aspects, be it pertaining to inherent stochasticity or due to observation noise. Our research group studies how to most efficiently model and describe the information contained in such data to allow for formulating powerful estimators and algorithms. The research results are applied to remote sensing, audio signal processing, as well as to spectroscopy.
Optimal transport in signal processing and inverse problems: we use the concept of optimal transport for inducing geometric structure to signal spaces and construct powerful tools for modeling and estimation.
Spatio-temporal modeling: efficient description of data that is supported in both space and time, for example, broad-band multi-sensor signals appearing in radar, sonar, and audio signal processing.
Misspecified modeling: the impact on estimation and data explanation performance when (sometimes deliberately) using a “wrong” model to describe data.
Optimal sampling: how to collect measurements to maximize the information content of the data, in particular for applications in which data collection is costly or time consuming.
Anton Björkman, PhD student
Linda Fabiani, PhD student
Yuyang Liu, PhD student
Rumeshika Pallewela, PhD student
David Sundström, PhD student (Lund University)