What is he best known for?
The fields of study he is best known for:
- Computer network
- Operating system
- The Internet
His primary scientific interests are in Computer network, Distributed computing, Key, Scalability and Operating system.
Many of his studies involve connections with topics such as The Internet and Computer network.
His Distributed computing research is multidisciplinary, relying on both Latency, Latency and Paxos.
His study in Key is interdisciplinary in nature, drawing from both Joule, Computer cluster, Replication, Parallel computing and Hash function.
His Scalability study incorporates themes from Throughput, Consistency model and Server.
His work deals with themes such as IP forwarding, Transmission Control Protocol, Fault detection and isolation, Key-based routing and Routing protocol, which intersect with Overlay network.
His most cited work include:
- Resilient overlay networks (1941 citations)
- Scaling distributed machine learning with the parameter server (846 citations)
- c-Through: part-time optics in data centers (555 citations)
What are the main themes of his work throughout his whole career to date?
David G. Andersen spends much of his time researching Computer network, Distributed computing, Operating system, Scalability and The Internet.
His Computer network study integrates concerns from other disciplines, such as Overlay network and Throughput.
His study on Load balancing is often connected to Causal consistency as part of broader study in Distributed computing.
His Operating system study combines topics in areas such as Key and Efficient energy use.
His Scalability study integrates concerns from other disciplines, such as Remote direct memory access, Ethernet and Multi-core processor.
As part of one scientific family, David G. Andersen deals mainly with the area of The Internet, narrowing it down to issues related to the Computer security, and often Internet protocol suite.
He most often published in these fields:
- Computer network (33.12%)
- Distributed computing (25.63%)
- Operating system (11.88%)
What were the highlights of his more recent work (between 2016-2021)?
- Data structure (8.12%)
- Algorithm (5.62%)
- Artificial intelligence (5.62%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Data structure, Algorithm, Artificial intelligence, Bloom filter and Distributed computing.
David G. Andersen interconnects Tree, Entropy, Encoder and Theoretical computer science in the investigation of issues within Data structure.
His Artificial intelligence research incorporates themes from Fuse, Frame and Machine learning.
David G. Andersen has included themes like Artificial neural network, Block, Memory management, Scheduling and Implementation in his Distributed computing study.
He studied Implementation and Microservices that intersect with Scalability.
His studies examine the connections between Scalability and genetics, as well as such issues in Replication, with regards to Computer network and Multi-core processor.
Between 2016 and 2021, his most popular works were:
- Cicada: Dependably Fast Multi-Core In-Memory Transactions (56 citations)
- SuRF: Practical Range Query Filtering with Fast Succinct Tries (53 citations)
- Datacenter RPCs can be general and fast (52 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Operating system
- The Internet
Distributed computing, Cloud computing, Algorithm, Scalability and Source code are his primary areas of study.
His Distributed computing research integrates issues from Frame rate, Enhanced Data Rates for GSM Evolution, Aggregate, Video processing and Transfer of learning.
His study in Cloud computing is interdisciplinary in nature, drawing from both Real-time computing, Backhaul, Analytics and Wide area network.
His biological study spans a wide range of topics, including Backpropagation, Image and Selection.
His Scalability research includes themes of Latency, Granularity, Microservices, Preemption and Scheduling.
His Source code study combines topics from a wide range of disciplines, such as Remote procedure call, Remote direct memory access, Computer network, State machine replication and Packet loss.
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