What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Programming language
Jason Mars focuses on Distributed computing, Real-time computing, Quality of service, Operating system and Server.
His study brings together the fields of Multi-core processor and Distributed computing.
His Real-time computing research is multidisciplinary, relying on both Cloud computing and Leverage.
His studies deal with areas such as Mobile device, Mobile computing and Collaborative intelligence as well as Cloud computing.
His Quality of service study combines topics in areas such as Boosting and Granularity.
Jason Mars combines subjects such as Batch processing and Bubble with his study of Server.
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?
Jason Mars spends much of his time researching Distributed computing, Artificial intelligence, Server, Quality of service and Multi-core processor.
Jason Mars brings together Distributed computing and Scale to produce work in his papers.
Within one scientific family, he focuses on topics pertaining to Machine learning under Artificial intelligence, and may sometimes address concerns connected to Representation.
His research investigates the connection with Server and areas like Workload which intersect with concerns in Scalability.
His Quality of service research incorporates elements of Temporal isolation among virtual machines, Real-time computing, Embedded system and Leverage.
His work investigates the relationship between Multi-core processor and topics such as Spec# that intersect with problems in Regression analysis and Predictive modelling.
He most often published in these fields:
- Distributed computing (32.95%)
- Artificial intelligence (18.18%)
- Server (17.05%)
What were the highlights of his more recent work (between 2018-2020)?
- Artificial intelligence (18.18%)
- Machine learning (12.50%)
- Training set (7.95%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Machine learning, Training set, Dialog box and Training.
His primary area of study in Artificial intelligence is in the field of Utterance.
His study in Utterance is interdisciplinary in nature, drawing from both Value, Similarity and Natural language processing.
Machine learning combines with fields such as Structure, Control system, Semantic vector and Metric in his work.
The study incorporates disciplines such as Annotation and Corpus based in addition to Training set.
His Dialog box research is multidisciplinary, incorporating perspectives in Anomaly detection, Data mining and Outlier.
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
His primary scientific interests are in Dialog box, Benchmark, Information retrieval, Class and Inference.
His Dialog box research integrates issues from Anomaly detection, Data mining and Outlier.
Jason Mars has researched Benchmark in several fields, including Field and Identification.
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.
