Official Fellow; Director of Studies in Computer Science
Reader in Algorithms and Probability
I grew up in a small town in central Germany, located in the "Sauerland" region, which may or may not be related to my family name. I completed my PhD in Computer Science at the University of Paderborn on efficient protocols for parallel networks. After two postdoctoral fellowships at Berkeley and Vancouver, I worked as a senior researcher and group leader at the Max Planck Institute for Informatics in Germany. Since 2013, I have been a University Lecturer in Cambridge. So far my lecturing experience here includes two undergraduate courses on Algorithms (Part I and Part II) as well as a graduate course on Machine Learning and Data Mining. I have also greatly enjoyed giving supervisions, and I do admire Cambridge's unique teaching environment combining lectures with one-to-one tutorials.
My research interests have always been at the intersection of computer science and mathematics, but more recently I have shifted my focus to the use of random walk based methods in computer science. This typically involves the simulation of small particles performing random walks on a network, for instance, a large social network, in order to quickly obtain some information and insights about its structure. For instance, we may want to find out whether two given nodes are connected, or more generally, whether we can partition the network into well-connected groups known as clustering. Since some of the networks we are dealing with contain billions of vertices, it is not feasible to perform a more traditional exhaustive search. Instead, we have resort to randomised, that is sampling-based approaches, which often includes running multiple random walks. In 2016 I have been awarded an ERC starting grant, which enables me to establish the first research group working in this area.