I’m chairing the research program committee of the ACM International Conference on Distributed and Event-Based Systems (DEBS) 2026 together with Sukanya Bhowmik (University of Potsdam). Paper deadline is 20th of Feburary. More information is on the website: https://2026.debs.org/
I’m an Associate Editor for PVLDB Volume 19 (VLDB 2026), covering the period from April 2025 to March 2026. Accepted papers from this volume will be presented at VLDB 2026, which will be held in Boston, USA.
Our paper titled “How Reliable Are Streams? End-to-End Processing-Guarantee Validation and Performance Benchmarking of Stream Processing Systems” has just been accepted for VLDB 2025!
Two papers on GNN and Federated Learning systems have been accepted at international top conferences: Nikolai Merkel, Pierre Toussing, Ruben Mayer, and Hans-Arno Jacobsen. “Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study.” 15 Pages. Accepted at VLDB 2025. Preprint available: https://arxiv.org/abs/2409.11129 Herbert Woisetschläger, Alexander Erben, Ruben Mayer, Shiqiang Wang, and Hans-ArnoContinue reading “Papers accepted at VLDB and Middleware conference”
Our paper titled “An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training” has been accepted at EDBT 2025! A preprint can be found on Arxiv: https://arxiv.org/abs/2308.15602
Two machine learning papers have been accepted: Herbert Woisetschläger, Alexander Erben, Shiqiang Wang, Ruben Mayer, and Hans-Arno Jacobsen. “A Survey on Efficient Federated Learning Methods for Foundation Model Training.” In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI ’24). Preprint: https://arxiv.org/abs/2401.04472 Herbert Woisetschläger, Alexander Erben, Shiqiang Wang, Ruben Mayer, and Hans-Arno Jacobsen.Continue reading “Papers at IJCAI and DEEM”
This year, I serve in many program committees of academic conferences. Among others, this is the 44th IEEE International Conference on Distributed Computing Systems, the 8th IEEE International Conference on Fog and Edge Computing, the 2024 International Joint Conference on Neural Networks, and the 18th ACM International Conference on Distributed and Event-based Systems.
Our paper titled “How Can We Train Deep Learning Models Across Clouds and Continents? An Experimental Study” has been accepted at PVLDB and is going to be presented at the VLDB 2024 conference! A preprint is avaibale online: https://arxiv.org/abs/2306.03163
Various papers have been accepted over the course of the summer: At the ACM International Conference on Middleware (Middleware ’23): At the ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys ’23): Congratulations to my successful PhD students!
Our paper titled “Energy vs Privacy: Estimating the Ecological Impact of FederatedLearning” has been accepted for publication at the 14th ACM International Conference on Future Energy Systems (e-Energy 2023)!
Our paper titled “The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey” has just been accepted for publication at ACM Computing Systems. In the paper, we draw parallels between distributed graph processing, deep learning and graph neural network systems. It brings together our differentContinue reading “Paper accepted at ACM Computing Surveys!”
Our paper titled “A Survey on Dataset Distillation: Approaches, Applications and Future Directions” has been accepted for publication at the 32nd International Joint Conference on Artificial Intelligence!
I’ve started a new position as a tenured professor of computer science at the University of Bayreuth. I’m going to build up a new research group there, the Chair of Data Systems. If you’re interested in a PhD or PostDoc position, please contact me!
Our paper titled “Partitioner Selection with EASE to Optimize Distributed Graph Processing” has been accepted at IEEE International Conference on Data Engineering (ICDE 2023). It explores the use of machine learning to automate the selection of the optimal graph partitioning algorithm for a given distributed graph processing problem.
I’m a member of the program committee of several conferences in 2023. Among others, ACM International Conference on Middleware 2023, and IEEE International Conference on Fog and Edge Computing 2023.
Ruben Mayer 