Prof. Dr. Felix Krahmer
Optimization and Data Analysis
Academic Career and Research Areas
The research of Professor Krahmer (b. 1981) focuses on the mathematical foundations of signal and image processing. In particular, his research agenda covers randomized sensing methods, especially in compressed sensing, dimension reduction, and analog to digital conversion. His research involves not only the theoretical analysis of such methods but also their application, such as in a project on the non-destructive testing of steel pipes.
Krahmer studied mathematics at the International University Bremen (now Jacobs University), the Université de Paris Sud and New York University's Courant Institute, where he acquired his doctoral degree in 2009 under the supervision of Percy Deift and Sinan Güntürk. After completing his PhD he moved to the Hausdorff Center for Mathematics in Bonn to work as an HCM postdoctoral researcher in the group of Holger Rauhut. From 2012 to 2015 he was a junior professor of Mathematical Data Analysis and, commencing 2014, also headed an Emmy Noether junior research group, both at the University of Göttingen. In April 2015 he joined TUM as an assistant professor.
- Supervisory Award of the International School of Applied Mathematics (3rd Prize) (2018)
- Member of the Emmy-Noether young investigator program of the German Science Foundation (since 2014)
- Charles Newman Fellow (2008-2009)
- Harold Grad Memorial Prize (2008)
- Cathleen Morawetz Fellow (2004-2009)
Key Publications (all publications)
Jung, P, Krahmer F, and Stöger, D: "Blind Demixing and Deconvolution at Near-Optimal Rate". IEEE Transactions on Information Theory. 2018; 64 (2): 704-727.
Krahmer F, Mendelson, S, Rauhut H: „Suprema of Chaos Processes and the Restricted Isometry Property“. Communications on Pure and Applied Mathematics. 2014; 67(11): 1877-1904.
Krahmer F, Ward R: „Stable and Robust Sampling Strategies for Compressive Imaging“. IEEE Transactions on Image Processing. 2014; 23(2): 612-622.
Deift P, Güntürk S, Krahmer F: „An Optimal Family of Exponentially Accurate One-Bit Sigma-Delta Quantization Schemes“. Communications on Pure and Applied Mathematics. 2011; 67(11): 883-919.
Krahmer F, Ward R: „New and Improved Johnson-Lindenstrauss Embeddings via the Restricted Isometry Property“. SIAM Journal on Mathematical Analysis. 2011; 43(3): 1269-1281.Listed as one of the 10 most popular SIMA articles