Geert Leus
Papers
6
Total Citations
78
H-Index
5
About
Geert Leus is a leading figure in signal processing, whose work bridges the gap between distributed optimization, graph-based learning, and multi-agent systems. His research focuses on developing algorithms that enable networks of nodes—from sensors to robots—to solve complex, time-varying problems in a decentralized manner. A cornerstone of his contributions is the introduction of a distributed asynchronous gradient-based algorithm for time-varying constrained optimization (2014, 34 citations), which allows nodes to update their variables independently, even under communication delays. This work has been foundational for real-time applications in dynamic environments. Leus has also made significant strides in graph signal processing, as seen in his highly cited papers "Graphs, Convolutions, and Neural Networks" (2020, 15 citations) and "From Graph Filters to Graph Neural Networks" (2020, 12 citations), where he leverages graph structure to design efficient learning frameworks. His impact extends to sparse Bayesian learning and multi-agent exploration, with algorithms that coordinate agents under sparsity constraints (2019, 5 citations). With over 30,000 total citations, Leus is a pioneer whose work continues to shape modern signal processing and machine learning.
Research Focus
Key Achievements
Top Papers
- 1Distributed asynchronous time-varying constrained optimization34 citations · 2014
- 2Graphs, Convolutions, and Neural Networks.15 citations · 2020
- 3From Graph Filters to Graph Neural Networks12 citations · 2020
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