What is he best known for?
The fields of study he is best known for:
- Computer network
- Operating system
- The Internet
His main research concerns Distributed computing, Computer network, Linear network coding, Server and Cloud computing.
His Distributed computing study integrates concerns from other disciplines, such as Quality of service, Overlay network and Routing protocol.
Baochun Li has included themes like Wireless, Wireless ad hoc network and Wireless network in his Computer network study.
His Linear network coding research is multidisciplinary, incorporating perspectives in Unicast, Multicast, Throughput, Redundancy and Coding.
His studies deal with areas such as Bandwidth, Multimedia, Upload, Game theory and Resource allocation as well as Server.
His Cloud computing study combines topics from a wide range of disciplines, such as Computer security, Exploit, Workload and Scalability.
His most cited work include:
- How Practical is Network Coding (1482 citations)
- A control-based middleware framework for quality-of-service adaptations (370 citations)
- Oruta: Privacy-Preserving Public Auditing for Shared Data in the Cloud (308 citations)
What are the main themes of his work throughout his whole career to date?
Computer network, Distributed computing, Cloud computing, Server and Linear network coding are his primary areas of study.
His Computer network research incorporates elements of Wireless and Throughput.
His Distributed computing research focuses on Distributed algorithm in particular.
His Cloud computing research is multidisciplinary, relying on both Computer security, Virtual machine and Database.
His research integrates issues of Scalability, Peer-to-peer, Multimedia, Upload and The Internet in his study of Server.
His Linear network coding research integrates issues from Node and Coding.
He most often published in these fields:
- Computer network (50.81%)
- Distributed computing (48.26%)
- Cloud computing (19.26%)
What were the highlights of his more recent work (between 2016-2021)?
- Distributed computing (48.26%)
- Cloud computing (19.26%)
- Computer network (50.81%)
In recent papers he was focusing on the following fields of study:
Baochun Li mainly investigates Distributed computing, Cloud computing, Computer network, Artificial intelligence and Server.
His Load balancing study in the realm of Distributed computing interacts with subjects such as Sizing.
His work on Cloud storage as part of his general Cloud computing study is frequently connected to Emulation, thereby bridging the divide between different branches of science.
His Computer network study combines topics in areas such as Throughput, Data processing and Erasure code.
His research investigates the connection with Artificial intelligence and areas like Machine learning which intersect with concerns in Crowdsourcing and Network congestion.
His studies in Server integrate themes in fields like Overhead, Distributed database, Distributed data store, Encryption and Information privacy.
Between 2016 and 2021, his most popular works were:
- Spotlight: Optimizing Device Placement for Training Deep Neural Networks. (31 citations)
- Optimizing Federated Learning on Non-IID Data with Reinforcement Learning (30 citations)
- Optimal Streaming Codes for Channels With Burst and Arbitrary Erasures (30 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Operating system
- The Internet
Baochun Li spends much of his time researching Distributed computing, Scheduling, Distributed database, Computer network and Artificial intelligence.
Baochun Li interconnects Data analysis and Reinforcement learning in the investigation of issues within Distributed computing.
His work carried out in the field of Scheduling brings together such families of science as Linear programming and Schedule.
The various areas that Baochun Li examines in his Distributed database study include Scalability, Cloud computing and Server.
His study in the field of Unicast also crosses realms of Dead reckoning.
Baochun Li interconnects Machine learning and Training in the investigation of issues within Artificial intelligence.
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