Krahmer obtained his PhD from New York University in 2009. From 2012, he was a junior professor and then, from 2014, headed the Emmy Noether Junior Research Group at the University of Göttingen. From 2015 to 2021, Krahmer was a tenure-track assistant professor at the Technical University of Munich. In addition, he served as chairman of the board of the Deutsche Gesellschaft Juniorprofessur e.V. from 2017 to 2021.

His work with Rachel Ward from the University of Texas on randomised dimension reduction methods and signal processing techniques is just as much a milestone in his career to date as his research with Ayush Bhandari from Imperial College London. With this, they initiated a new research direction in which a number of international researchers across several disciplines are now participating. We asked Professor Krahmer a few questions.

The efficient exchange of signals is becoming increasingly important in our interconnected world, and machine learning methods play a key role in this. At the same time, we are still far from fully understanding how they work. In my team, you can help to advance this understanding. At the same time, my research area is methodologically very diverse: it brings together signal processing, imaging, algorithms and optimisation methods, statistical learning theory, probability theory and many other fields.

My research lies precisely at the intersection of electrical and information engineering, mathematics and computer science. Before I moved, I was a professor of mathematics; now I am in electrical and information engineering, and several of my former PhD students have since gone on to work in computer science. This therefore fits very well with TU Darmstadt’s interdisciplinary focus. But even beyond these three fields, my research has many applications – for instance, a theoretical understanding of how signal processing and artificial intelligence work is particularly important in critical applications such as medicine, where ‘we’ve tried it and it seems to work’ is not a sufficient basis.

Even though it’s not a specific department: I’d love to spend a day shadowing the Chancellor; I’m sure I’d learn a lot about how the university works.

… get more involved in student representation. It wasn’t until later that I realised just how much influence you can have through it.

… spending time with my family. But singing in the jazz choir is also a great way for me to unwind.