What is he best known for?
The fields of study he is best known for:
- Programming language
- Software
- Operating system
His scientific interests lie mostly in Software engineering, Software development, Software system, Event and Distributed computing.
His Software engineering study frequently intersects with other fields, such as Software architecture description.
His work deals with themes such as Software architecture, Architecture description language, Reference architecture and Assertion, which intersect with Software development.
Software system is a subfield of Programming language that he studies.
David S. Rosenblum combines subjects such as The Internet and Asynchronous communication with his study of Event.
As a part of the same scientific study, David S. Rosenblum usually deals with the The Internet, concentrating on Scalability and frequently concerns with Service, Computer network, Server, Wide area network and Client–server model.
His most cited work include:
- Design and evaluation of a wide-area event notification service (1367 citations)
- An architecture-based approach to self-adaptive software (884 citations)
- From action to activity (395 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Software engineering, Software system, Distributed computing, Software and Programming language.
His Software engineering research is multidisciplinary, incorporating elements of Software architecture description, Software development, Software construction, Systems engineering and Software architecture.
He focuses mostly in the field of Software construction, narrowing it down to matters related to Component-based software engineering and, in some cases, Component.
His Software system research is multidisciplinary, relying on both Reliability engineering, Scalability and Software quality.
His Scalability study combines topics from a wide range of disciplines, such as Computer network, Service and The Internet.
The Distributed computing study combines topics in areas such as Ubiquitous computing, Event and Adaptation.
He most often published in these fields:
- Software engineering (28.44%)
- Software system (17.33%)
- Distributed computing (12.89%)
What were the highlights of his more recent work (between 2013-2021)?
- Artificial intelligence (9.33%)
- Machine learning (7.56%)
- Markov chain (4.44%)
In recent papers he was focusing on the following fields of study:
David S. Rosenblum mainly investigates Artificial intelligence, Machine learning, Markov chain, Probabilistic logic and Theoretical computer science.
His biological study spans a wide range of topics, including Software deployment, Social network, Smart city, Data science and Pattern recognition.
In his study, Domain, Scalability, Artificial neural network, Conflation and Unsupervised learning is inextricably linked to Embedding, which falls within the broad field of Machine learning.
David S. Rosenblum combines subjects such as Software system, Mathematical optimization, Reachability and Applied mathematics with his study of Markov chain.
His Probabilistic logic research incorporates elements of Algorithm, Latent variable and Data mining.
His study looks at the relationship between Software walkthrough and topics such as The Internet, which overlap with Software engineering.
Between 2013 and 2021, his most popular works were:
- From action to activity (395 citations)
- Action2Activity: recognizing complex activities from sensor data (283 citations)
- Fortune teller: predicting your career path (200 citations)
In his most recent research, the most cited papers focused on:
- Software
- Programming language
- Operating system
His main research concerns Artificial intelligence, Machine learning, Activity recognition, Software and Data mining.
David S. Rosenblum has researched Artificial intelligence in several fields, including Social network and Pattern recognition.
His study looks at the relationship between Activity recognition and fields such as Relation, as well as how they intersect with chemical problems.
The concepts of his Software study are interwoven with issues in Uncertainty quantification and Hidden Markov model.
His studies deal with areas such as Parametric statistics and Random variable as well as Data mining.
His research on Variety frequently links to adjacent areas such as Software engineering.
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