I am a Postdoctoral Fellow at the Department of Applied Mathematics and Statistics at Johns Hopkins University, mentored by Mauro Maggioni. Before that, I completed a Ph.D. in Mathematics at Technical University of Munich, advised by Felix Krahmer, in 2019. I also obtained M.Sc. and B.Sc. degrees in Mathematics from TU Munich in 2015 and 2013, respectively.
My research focuses on scalable, efficient and reliable algorithms and models for machine learning and data science. I am interested in the theory and practice of addressing computational and statistical challenges arising from models involving sparsity, graph or low-rank structures with efficient optimization methods. To this end, I leverage mathematics ranging from high-dimensional probability, applied and computational harmonic analysis, non-convex optimization to numerical linear algebra in my research.
I will join the Department of Computer Science of University of North Carolina at Charlotte in Fall 2022.
I am actively looking for highly motivated students interested in the foundations of machine learning! I am conducting both theoretical and empirical research. If you have similar research interests and are interested in a Ph.D. at UNC Charlotte, please apply here, mention my name in your application and let me know.
If you are a master’s student at UNC Charlotte and interested in a research project or thesis, shoot me an email.
If you are an undergraduate student and would like to conduct research, please contact me via email.
My last name can also be written as Kuemmerle.