Professor Klaus Nordhausen develops modern multivariate statistical methods to analyze high-dimensional and large datasets in different fields.
What are your research topics?
I develop statistical methods that help people make sense of complex and high-dimensional data - for example, information collected from many sensors, locations, or points in time. Much of today's data contain both useful signals and irrelevant noise, and my work focuses on separating the two. By finding the essential patterns hidden in noisy or incomplete information, these methods make it easier to monitor systems, detect changes, and make reliable decisions.
My research builds on concepts such as Invariant Coordinate Selection (ICS), Blind Source Separation (BSS), and related multivariate decomposition techniques, with applications in time-series, spatial, and spatio-temporal settings. It combines theoretical development-including advances in robust scatter matrices, independence measures, and identifiability theory-with computational tools and practical case studies. These approaches are applicable across diverse fields, from industrial quality control and environmental monitoring to medical research, wherever large and complex datasets must be transformed into clear and trustworthy insights.
Where and how does the topic of your research have an impact?
Statistical methods for understanding complex data are becoming essential in many areas of modern life. My research helps improve how data are analyzed in fields such as industrial maintenance, environmental monitoring, and medical research. For example, better statistical tools can detect early signs of equipment failure, identify unusual environmental patterns, or highlight important biological signals.
By making data analysis more reliable and interpretable, this work supports better decisions, more efficient use of resources, and helps to transform large amounts of data into knowledge that benefits society.
What inspires you in your field right now?
Data have become part of almost everything we do. From environmental sensors and medical devices to industrial production and everyday technology, data are being collected everywhere - and they're becoming larger, more complex, and more interconnected. This creates exciting challenges for statisticians: we need to develop methods that can find the meaningful patterns hidden in all that information. It's rewarding to see how statistical thinking is becoming essential across so many areas, helping people turn raw data into understanding and better decisions.
