What is he best known for?
The fields of study he is best known for:
- Operating system
- Programming language
- Artificial intelligence
His primary areas of study are Parallel computing, Compiler, Distributed computing, Programming language and Java.
His Parallel computing research integrates issues from Optimizing compiler, Software, Overhead and Automatic parallelization.
His study in Compiler is interdisciplinary in nature, drawing from both Thread, Central processing unit, Programmer and Fortran.
His Thread research includes themes of Method, Reachability, Semaphore, PowerPC and Escape analysis.
Manish Gupta has included themes like Application server, Protocol, Redundancy, Synchronization and Rollback in his Distributed computing study.
As part of one scientific family, Manish Gupta deals mainly with the area of Java, narrowing it down to issues related to the Heap, and often Memory management and Region-based memory management.
His most cited work include:
- An Overview of the BlueGene/L Supercomputer (498 citations)
- Escape analysis for Java (488 citations)
- Blue Gene: a vision for protein science using a petaflop supercomputer (253 citations)
What are the main themes of his work throughout his whole career to date?
Manish Gupta mainly investigates Parallel computing, Compiler, Programming language, Distributed computing and Java.
His Parallel computing research is multidisciplinary, relying on both Scalability, Computation and Automatic parallelization.
His research on Compiler concerns the broader Operating system.
His Distributed computing research incorporates themes from Software and Component.
His Java study which covers Heap that intersects with Garbage collection.
His research integrates issues of Java concurrency and Scala in his study of Java annotation.
He most often published in these fields:
- Parallel computing (28.04%)
- Compiler (18.69%)
- Programming language (15.89%)
What were the highlights of his more recent work (between 2011-2021)?
- Artificial intelligence (7.48%)
- Virtual machine (7.01%)
- Computer network (6.07%)
In recent papers he was focusing on the following fields of study:
Manish Gupta spends much of his time researching Artificial intelligence, Virtual machine, Computer network, Social media and Operating system.
His Artificial intelligence research includes elements of Machine learning, Task, Data structure and Natural language processing.
His study in Virtual machine is interdisciplinary in nature, drawing from both Image, Software, Software deployment and Ranking.
His Computer network research focuses on subjects like Cloud computing, which are linked to Server and Risk analysis.
His studies deal with areas such as Information retrieval and Data science as well as Social media.
Service is closely connected to Real-time computing in his research, which is encompassed under the umbrella topic of Event.
Between 2011 and 2021, his most popular works were:
- Predicting future popularity trend of events in microblogging platforms (48 citations)
- FERMI at SemEval-2019 Task 5: Using Sentence embeddings to Identify Hate Speech Against Immigrants and Women in Twitter. (23 citations)
- Automatic pipeline parallelization of sequential code (21 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- Artificial intelligence
Manish Gupta mostly deals with Virtual machine, Operating system, Server, Computer network and Artificial intelligence.
His Virtual machine research includes themes of Scheduling, Template method pattern, Software and Set.
His work on Software system as part of general Software study is frequently linked to Time windows, therefore connecting diverse disciplines of science.
Manish Gupta studies Provisioning which is a part of Operating system.
His studies examine the connections between Computer network and genetics, as well as such issues in Cloud computing, with regards to Cache.
His Artificial intelligence research is multidisciplinary, relying on both Natural language processing, Task, Data processing system, Machine learning and Pattern recognition.
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