Preisträger*innen – UNIVIE Forschungspreise für Studierende 2025/2026

Iterative solvers for large, sparse linear systems are widely used on powerful supercomputers. When solving very large problems, communication poses a significant bottleneck, which has prompted the development of communication-avoiding algorithms. We investigate how these algorithms can be combined with novel randomization strategies and introduce a new approach that achieves up to four times the performance of the current state-of-the-art on a modern supercomputer.

Deep clustering uses neural networks to automatically find patterns and group similar data together without needing labeled examples. In both works, we propose to improve existing deep clustering models using periodic resets of the network weights during training. We demonstrate that established centroid-based deep clustering methods achieve substantially higher accuracy when trained with our novel algorithms that overcome the performance plateaus and enhance the training with pseudo-labels. Our approaches use weight resets to avoid early over-commitment to initial cluster assignments and iteratively refine pre-trained models using their own clustering assignments. In experiments across multiple benchmark datasets, we observe consistent performance gains that deliver foundational contributions in deep clustering.