What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Programming language
His primary areas of investigation include Distributed computing, Operating system, Multi-core processor, Computation and Dataflow.
His studies deal with areas such as Artificial neural network, Deep learning, CUDA and Mobile device as well as Distributed computing.
His studies in CUDA integrate themes in fields like Information extraction, Theano, Interface and Computational learning theory.
In the field of Operating system, his study on Embedded operating system and Backward compatibility overlaps with subjects such as Hardware-assisted virtualization and Structure.
The concepts of his Multi-core processor study are interwoven with issues in Machine learning, Instruction set, Artificial intelligence and Memory management.
His Computation research integrates issues from Timestamp, Maintainability, Stream processing and Programming paradigm.
His most cited work include:
- Xen and the art of virtualization (6136 citations)
- TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (5091 citations)
- TensorFlow: a system for large-scale machine learning (4961 citations)
What are the main themes of his work throughout his whole career to date?
Paul Barham mainly investigates Distributed computing, Operating system, Computer network, Artificial intelligence and Computer security.
His work carried out in the field of Distributed computing brings together such families of science as Multi-core processor, Barrelfish, Service, Computation and Component.
His work on Scheduling and Instrumentation as part of his general Operating system study is frequently connected to Hypervisor and Memory protection, thereby bridging the divide between different branches of science.
Paul Barham interconnects Overlay network and The Internet in the investigation of issues within Computer network.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning, Mobile device and Computer vision.
Paul Barham combines subjects such as Inference, CUDA and Dataflow with his study of Machine learning.
He most often published in these fields:
- Distributed computing (25.61%)
- Operating system (18.29%)
- Computer network (18.29%)
What were the highlights of his more recent work (between 2016-2019)?
- Artificial intelligence (13.41%)
- Programming paradigm (4.88%)
- Machine learning (8.54%)
In recent papers he was focusing on the following fields of study:
Paul Barham mainly focuses on Artificial intelligence, Programming paradigm, Machine learning, Reinforcement learning and Control flow.
His Artificial intelligence research incorporates elements of Mobile device and Computer vision.
His Programming paradigm study integrates concerns from other disciplines, such as Field, Maintainability and Compiler.
Paul Barham performs multidisciplinary study on Machine learning and Modularity in his works.
His study in Reinforcement learning is interdisciplinary in nature, drawing from both Scalability, Recurrent neural network, Automatic differentiation, Semantics and Dataflow.
His study ties his expertise on Inference together with the subject of Control flow.
Between 2016 and 2019, his most popular works were:
- Dynamic control flow in large-scale machine learning (33 citations)
- Machine Learning Systems are Stuck in a Rut (23 citations)
- METHOD FOR DISPLAYING PRE-RENDERED IMAGES WITH LATE STAGE GRAPHICAL ADJUSTMENTS, AND CORRESPONDING MOBILE DEVICE (1 citations)
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