What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Programming language
His main research concerns Distributed computing, Real-time computing, Operating system, Server and Quality of service.
His Distributed computing research includes themes of Shared resource and Multi-core processor.
The study incorporates disciplines such as Boosting, Cloud computing and Granularity in addition to Real-time computing.
Lingjia Tang has included themes like Mobile device, Mobile computing and Collaborative intelligence in his Cloud computing study.
His work deals with themes such as Web service, Service and Artificial intelligence, which intersect with Operating system.
The various areas that Lingjia Tang examines in his Server study include Batch processing, Computer architecture and Bubble.
His most cited work include:
- Bubble-Up: increasing utilization in modern warehouse scale computers via sensible co-locations (438 citations)
- Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge (371 citations)
- Bubble-flux: precise online QoS management for increased utilization in warehouse scale computers (262 citations)
What are the main themes of his work throughout his whole career to date?
Lingjia Tang mostly deals with Distributed computing, Artificial intelligence, Server, Quality of service and Machine learning.
His Distributed computing study combines topics in areas such as Shared resource, Cloud computing, Multi-core processor and Profiling.
His work on Cloud gaming as part of general Cloud computing study is frequently linked to Benchmarking, therefore connecting diverse disciplines of science.
As part of the same scientific family, Lingjia Tang usually focuses on Server, concentrating on Workload and intersecting with Scalability.
His work investigates the relationship between Quality of service and topics such as Real-time computing that intersect with problems in Boosting.
In the field of Machine learning, his study on Statistical classification overlaps with subjects such as Control system and Structure.
He most often published in these fields:
- Distributed computing (31.51%)
- Artificial intelligence (20.55%)
- Server (20.55%)
What were the highlights of his more recent work (between 2018-2020)?
- Artificial intelligence (20.55%)
- Training set (9.59%)
- Machine learning (13.70%)
In recent papers he was focusing on the following fields of study:
Lingjia Tang focuses on Artificial intelligence, Training set, Machine learning, Dialog box and Training.
His Training set study integrates concerns from other disciplines, such as Annotation, Utterance and Natural language processing.
His research in Natural language processing intersects with topics in Segmentation, Skeleton and Security token.
Machine learning is often connected to Corpus based in his work.
The various areas that he examines in his Dialog box study include Anomaly detection, Data mining and Outlier.
His work often combines Training and Representation studies.
Between 2018 and 2020, his most popular works were:
- GrandSLAm: Guaranteeing SLAs for Jobs in Microservices Execution Frameworks (34 citations)
- An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction (18 citations)
- An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction. (8 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Programming language
Lingjia Tang mainly focuses on Dialog box, Identification, Field, Inference and Class.
His research integrates issues of Anomaly detection, Outlier and Robustness in his study of Dialog box.
The Identification study combines topics in areas such as Information retrieval and Benchmark.
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