What is he best known for?
The fields of study he is best known for:
- Programming language
- Computer network
- Algorithm
Distributed computing, Dataflow, Parallel computing, Synchronous Data Flow and Theoretical computer science are his primary areas of study.
His Distributed computing study incorporates themes from Implementation, Kahn process networks, Programming paradigm and Graph.
His research on Dataflow often connects related topics like Computational complexity theory.
His Parallel computing research incorporates elements of Digital signal processing, Scheduling and System recovery.
As a part of the same scientific study, Marc Geilen usually deals with the Synchronous Data Flow, concentrating on Data flow diagram and frequently concerns with Data compression and Real-time computing.
His Theoretical computer science research includes elements of Consistency, Algorithm design and Data-flow analysis.
His most cited work include:
- Throughput Analysis of Synchronous Data Flow Graphs (234 citations)
- SDF^3: SDF For Free (231 citations)
- Exploring trade-offs in buffer requirements and throughput constraints for synchronous dataflow graphs (160 citations)
What are the main themes of his work throughout his whole career to date?
Marc Geilen spends much of his time researching Distributed computing, Dataflow, Parallel computing, Theoretical computer science and Scheduling.
His work deals with themes such as Resource, Wireless sensor network, Computer network and Design space exploration, which intersect with Distributed computing.
As a part of the same scientific family, Marc Geilen mostly works in the field of Dataflow, focusing on Computation and, on occasion, Mathematical optimization.
His research in Parallel computing intersects with topics in Algorithm design, Digital signal processing and Synchronous Data Flow.
His Theoretical computer science research incorporates themes from Finite-state machine and Correctness.
His research in the fields of Processor scheduling overlaps with other disciplines such as Schedule.
He most often published in these fields:
- Distributed computing (30.10%)
- Dataflow (29.59%)
- Parallel computing (20.41%)
What were the highlights of his more recent work (between 2017-2020)?
- Dataflow (29.59%)
- Distributed computing (30.10%)
- Domain (4.08%)
In recent papers he was focusing on the following fields of study:
Marc Geilen mainly investigates Dataflow, Distributed computing, Domain, Mathematical optimization and Resource management.
In his papers, Marc Geilen integrates diverse fields, such as Dataflow and Throughput.
His Distributed computing research is multidisciplinary, relying on both Pareto principle and Resource.
The study incorporates disciplines such as Scheduling, Job shop scheduling and Asynchronous communication in addition to Mathematical optimization.
His Scheduling research includes themes of Model checking, Time division multiple access, Real-time computing and Reachability.
In his research, Parallel computing is intimately related to Granularity, which falls under the overarching field of Data-flow analysis.
Between 2017 and 2020, his most popular works were:
- The FitOptiVis ECSEL project: highly efficient distributed embedded image/video processing in cyber-physical systems (13 citations)
- Partial-Order Reduction for Performance Analysis of Max-Plus Timed Systems (4 citations)
- Scalable Analysis for Multi-Scale Dataflow Models (4 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Computer network
- Algorithm
His primary areas of study are Distributed computing, Dataflow, Video processing, Domain and Cyber-physical system.
His Distributed computing study integrates concerns from other disciplines, such as Complex system, Component-based software engineering and Convolution.
His study in Dataflow is interdisciplinary in nature, drawing from both Model of computation, Representation and Modular design.
Marc Geilen combines subjects such as Data-flow analysis, Static timing analysis, Parallel computing, Iterative method and Granularity with his study of Video processing.
The concepts of his Domain study are interwoven with issues in Mathematical optimization, Minification, Correctness, Dependency and Computation.
His Cyber-physical system investigation overlaps with Quality, Reliability engineering, Machine control, Control and Control theory.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
