Marc Roth

Marc Roth is a Lecturer in Theoretical Computer Science at the School of Electronic Engineering and Computer Science, Queen Mary University of London. He is also an Associate Member of the Department of Computer Science at the University of Oxford, reflecting his ongoing research collaboration with one of the leading computer science departments in the world.

His research centers on computational counting problems, particularly focusing on the multivariate and exact complexity of problems that are intractable under classical computation models. He investigates motif counting in higher-order networks, which has applications in data mining, bioinformatics, genetics, and artificial intelligence. His work bridges theoretical algorithmics with practical implications, including the expressive power of graph neural networks and logical characterizations in descriptive complexity.

Marc Roth's research integrates advanced algorithmic techniques such as parameterized complexity, fine-grained complexity, randomized algorithms, and approximation methods. His recent focus on higher-order structures extends traditional graph-based analysis to relational data, opening new avenues for theoretical and applied network science.

While no specific awards are listed, his active supervision of PhD research and prior prestigious affiliations suggest recognition in the theoretical computer science community.

He advises PhD students, as evidenced by his advertised studentship on motif counting in higher-order networks. Though no grants are explicitly mentioned, the funded PhD position indicates involvement in research funding. His academic journey includes a PhD from Saarland University under Holger Dell, a Junior Research Fellowship at Merton College, Oxford, and a Senior Research Associate role in Algorithms and Complexity Theory at Oxford’s Department of Computer Science.

Marc Roth is based at the Peter Landin Building on the Mile End Campus, where he holds office hours on Fridays. His work continues to advance the foundations of computational complexity and its applications in modern data science.