Jimmy Olsson

Mathematical statistics deals with mathematical theory for analyzing and modeling random processes and data in technical, biological and financial systems. Today, computer-based technology makes it possible to collect ever larger and more complex data sets. This has led to a new field, data analysis, where statistics and computer science meet. The subject of mathematical statistics plays an important role in this field, as it provides the mathematical tools needed to describe the quality and uncertainty of statistical estimators and often highly advanced computer algorithms.

In his research, Jimmy Olsson focuses on so-called generative models, where data are modeled as noisy or incomplete observations of unobserved, latent variables, and associated statistical computational methods. In addition, the models he studies are usually dynamic in the sense that observations are time-dependent and made in real time. These models are especially important in artificial intelligence (AI) and machine learning, as AI is often used to find hidden patterns in such data streams.

Jimmy Olsson’s research contributes to the basic understanding and development of, for example, general state-space hidden Markov models, sequential Monte Carlo methods and Markov chain Monte Carlo methods.