Welcome, Professor Marie Düker

What have been the most important stages in your academic career?

My journey into research did not begin with a long-term plan, but rather in the sixth semester of my mathematics degree, when I took on a position as a student assistant at the Institute of Hydrology at Ruhr University Bochum. It was there that I experienced for the first time how a specific practical problem could be transformed into a mathematical research question. Working with real measurement data sparked my interest in statistics and had a lasting influence on my academic path.

While pursuing my Ph.D. in mathematics at Ruhr University Bochum, I spent a long time as a visiting scholar at the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill (USA). Through new course content and formats, different perspectives and a more discussion-oriented teaching culture, I developed a broader perspective on statistics. Particularly valuable are the friendships and collaborations that developed during this time and continue to this day.

After completing my Ph.D., I worked as a postdoctoral researcher at the Department of Statistics and Data Science at Cornell University (USA). I then served as an assistant professor at Friedrich-Alexander University Erlangen-Nuremberg before moving to the Technical University of Munich (TUM).

What are your main areas of research?

I develop statistical theories and methods for complex data – in particular, high-dimensional, time-dependent and functional data. With high-dimensional data, a large number of features are considered simultaneously. Time series involve observations that are interrelated over time. Functional data, in turn, consist not only of individual measurements but also of entire curves or time trends, for example.

I am particularly interested in how reliable conclusions can be drawn even when classical statistical assumptions are not met – for example, in cases of long-term dependencies, changing data structures or structural breaks. My goal is to develop methods that are mathematically sound and, at the same time, useful for real-world scientific problems – such as in econometrics, neuroscience, chemistry and ecology.

What are you most looking forward to in your new position at the TUM? 

I am particularly looking forward to collaborating with students and colleagues, as well as to TUM’s vibrant, inspiring research environment. It is precisely through interdisciplinary exchange that new questions and perspectives often arise – and perhaps areas of research that are not yet on my agenda. In addition, I would like to get involved in projects promoting gender equality and supporting early-career researchers.

I also see an opportunity in teaching to explore new approaches and develop innovative courses. The question of what good mathematics teaching might look like in the age of generative AI is particularly exciting: Which tasks foster genuine understanding? How do students learn to critically evaluate results? And in what areas can AI provide meaningful support? 

What was your biggest “aha” moment in your scientific career? 

While working on high-dimensional statistics, I realized early on just how creative mathematical research is. In high-dimensional spaces, our usual geometric intuition often no longer applies. We therefore have to develop new concepts, try out different perspectives and sometimes make completely unexpected connections. It is precisely this search for innovative methods and ways of thinking, as well as the creative approach to complex problems, that makes research so fascinating to me.

What is at the top of your personal bucket list?     

First, I’d like to get to know Munich and the mountains right on my doorstep even better – through hiking and climbing. I’m also especially looking forward to showing my nieces around Munich and rediscovering the city through their eyes.