Nikolai Merkel
Technical University of Munich (TUM)
Institute of Informatics
Boltzmannstr. 3, 85748 Garching b. München
nikolai.merkel@tum.de
I did my Ph.D. in Computer Science at the Technical University of Munich (TUM)
under the supervision of Prof. Dr. Hans-Arno Jacobsen. My research interests focus on improving
the performance of data systems, with a particular focus on large-scale (distributed) Graph Neural
Network (GNN) systems and distributed graph processing systems. I explore optimizations such as
graph partitioning and graph reordering to enhance data locality and accelerate GNN training.
Additionally, I investigate the use of machine learning to automate and optimize graph processing
workflows, including selecting the best graph partitioning algorithm for specific workloads. While
my previous work has centered on graph-based systems, I am also eager to explore broader areas,
such as machine learning systems, distributed data processing, and leveraging machine learning to
optimize data systems.
Education
Ph.D., Technical University of Munich (TUM), Submitted in December 2024, Defended in July 2025
M.Sc., Technical University of Munich (TUM), Completed in June 2020
Publications
Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study
Nikolai Merkel, Pierre Toussing, Ruben Mayer, and Hans-Arno Jacobsen
In Proceedings of the 51st International Conference on Very Large Data Bases (VLDB '25),
September 1-5, London, United Kingdom. Accepted.
Paper
An Experimental Comparison of Partitioning Strategies for Distributed Graph Neural Network Training
Nikolai Merkel, Daniel Stoll, Ruben Mayer, and Hans-Arno Jacobsen
In Proceedings of the 28th International Conference on Extending Database Technology (EDBT '25),
March 25-28, Barcelona, Spain. Accepted.
Paper
Partitioner Selection with EASE to Optimize Distributed Graph Processing
Nikolai Merkel, Ruben Mayer, Tawkir Ahmed Fakir, and Hans-Arno Jacobsen
In Proceedings of the 2023 IEEE 39th International Conference on Data Engineering (ICDE '23),
April 3-7, 2023, Anaheim, California, USA. Accepted.
Paper
Scholarships
Max Weber Program of the state of Bavaria
Selected for an elite scholarship program supporting top-performing students at Bavarian universities with mentoring, training, and financial support.
Scholarship from the German National Academic Foundation
Awarded by Germany’s most prestigious scholarship foundation for outstanding academic achievement, intellectual ability, and social engagement.
Professional Service
Reviewer
- ACM Transactions on Knowledge Discovery from Data (TKDD)
- IEEE International Conference on Data Engineering (ICDE)
External Reviewer
- Technology Conference on Performance Evaluation and Benchmarking in conjunction with VLDB
- ACM/IFIP International Middleware Conference (Middleware)
Teaching
Advanced Master Seminar: Large-scale Graph Processing and Graph Partitioning
In my seminar, students explore topics in large-scale graph processing, including distributed
GNN training, distributed graph processing, out-of-core graph processing, in-memory graph
partitioning, streaming graph partitioning, temporal graph processing, hypergraph processing,
and graph generators.
- Summer Semester 2025
- Winter Semester 2024/25
- Summer Semester 2024
- Winter Semester 2023/24
- Summer Semester 2023
- Winter Semester 2022/23
- Summer Semester 2022
- Winter Semester 2021/22
Exercises for Operating Systems and Hardware-oriented Programming