What is he best known for?
The fields of study he is best known for:
- Computer network
- Artificial intelligence
- Machine learning
Matthias Grossglauser spends much of his time researching Computer network, Wireless ad hoc network, Distributed computing, Network packet and Real-time computing.
His studies link Wireless network with Computer network.
His studies in Wireless network integrate themes in fields like Network topology, Cellular network and Throughput.
His Wireless ad hoc network study focuses on Vehicular ad hoc network in particular.
Matthias Grossglauser has included themes like Ad hoc wireless distribution service and Optimized Link State Routing Protocol in his Vehicular ad hoc network study.
His Distributed computing research is multidisciplinary, incorporating perspectives in Mobile ad hoc network, Node, Routing protocol and Mobile radio.
His most cited work include:
- Mobility increases the capacity of ad hoc wireless networks (2335 citations)
- Mobility increases the capacity of ad-hoc wireless networks (555 citations)
- Age matters: efficient route discovery in mobile ad hoc networks using encounter ages (378 citations)
What are the main themes of his work throughout his whole career to date?
Matthias Grossglauser focuses on Computer network, Artificial intelligence, Distributed computing, Machine learning and Network packet.
His research in Computer network intersects with topics in Wireless ad hoc network and Wireless network.
His Wireless ad hoc network study integrates concerns from other disciplines, such as Mobile ad hoc network and Mobility model.
His Wireless network research is multidisciplinary, incorporating elements of Hierarchical routing and Throughput.
His Distributed computing study which covers Node that intersects with Routing.
His Network packet research incorporates themes from Traffic generation model, Traffic flow and Channel capacity.
He most often published in these fields:
- Computer network (34.09%)
- Artificial intelligence (21.97%)
- Distributed computing (18.94%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (21.97%)
- Machine learning (16.67%)
- Pairwise comparison (6.82%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Machine learning, Pairwise comparison, Embedding and Statistical model.
His work in the fields of Machine learning, such as Multivariate statistics, intersects with other areas such as Set.
He has researched Pairwise comparison in several fields, including Scalability and Theoretical computer science.
While working on this project, Matthias Grossglauser studies both Scalability and Set.
His Embedding course of study focuses on Metric and Class.
His Algorithm research includes elements of Node, Sequence and Graph alignment.
Between 2017 and 2021, his most popular works were:
- Analysis of a Canonical Labeling Algorithm for the Alignment of Correlated Erdős-Rényi Graphs (10 citations)
- On the Performance of a Canonical Labeling for Matching Correlated Erdős-Rényi Graphs. (10 citations)
- Learning Hawkes Processes Under Synchronization Noise (9 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Artificial intelligence
- Machine learning
His primary scientific interests are in Artificial intelligence, Graph, Subspace topology, Random graph and Bipartite graph.
His Artificial intelligence study incorporates themes from Machine learning and Metric.
The Transfer of learning and Regularization research Matthias Grossglauser does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Point and Domain, therefore creating a link between diverse domains of science.
His Subspace topology research incorporates elements of Aggregate, Computer vision and Pattern recognition.
Matthias Grossglauser combines subjects such as Graph isomorphism, Vertex and Algorithm with his study of Random graph.
The concepts of his Bipartite graph study are interwoven with issues in Matching, Blossom algorithm and Binary logarithm.
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