What is he best known for?
The fields of study he is best known for:
- The Internet
- Computer network
- World Wide Web
The Internet, Traffic classification, Upload, Popularity and Data mining are his primary areas of study.
His The Internet research is multidisciplinary, incorporating elements of Zipf's law, Proxy, Traffic analysis and Database.
Anirban Mahanti has researched Traffic classification in several fields, including Traffic generation model, BitTorrent and Internet traffic.
His Upload research is multidisciplinary, incorporating perspectives in Download, Real Time Streaming Protocol and Social network.
The study incorporates disciplines such as Semi-supervised learning, Cluster analysis, Machine learning and Artificial intelligence in addition to Data mining.
His work on Video sharing as part of general Multimedia research is frequently linked to User-generated content, thereby connecting diverse disciplines of science.
His most cited work include:
- Youtube traffic characterization: a view from the edge (778 citations)
- Traffic classification using clustering algorithms (558 citations)
- Offline/realtime traffic classification using semi-supervised learning (228 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include World Wide Web, Computer network, The Internet, Popularity and Multimedia.
His studies in World Wide Web integrate themes in fields like Snapshot and Internet privacy.
His Computer network research incorporates themes from Scalability and Distributed computing.
Anirban Mahanti focuses mostly in the field of The Internet, narrowing it down to matters related to Data mining and, in some cases, Traffic classification, Internet traffic and Cluster analysis.
His research in Internet traffic intersects with topics in Web application, Machine learning, Artificial intelligence and Identification.
His Multimedia course of study focuses on Service and Content management.
He most often published in these fields:
- World Wide Web (30.69%)
- Computer network (28.71%)
- The Internet (16.83%)
What were the highlights of his more recent work (between 2013-2018)?
- World Wide Web (30.69%)
- Multimedia (15.84%)
- Internet privacy (13.86%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are World Wide Web, Multimedia, Internet privacy, JavaScript and Web page.
The concepts of his World Wide Web study are interwoven with issues in Personally identifiable information, Focus and Snapshot.
His Focus research incorporates elements of Identity provider and End user.
His work is dedicated to discovering how Multimedia, Service are connected with Bandwidth and Communication channel and other disciplines.
He studied Internet privacy and Identity management that intersect with Information sharing, Control and Information leakage.
His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Data mining and Pattern recognition.
Between 2013 and 2018, his most popular works were:
- Predicting user traits from a snapshot of apps installed on a smartphone (99 citations)
- Game Theoretic Model Predictive Control for Distributed Energy Demand-Side Management (56 citations)
- A Dynamic Game for Electricity Load Management in Neighborhood Area Networks (52 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- The Internet
- World Wide Web
Anirban Mahanti spends much of his time researching World Wide Web, Multimedia, Popularity, Repeated game and Load management.
Anirban Mahanti combines subjects such as Key and Snapshot with his study of World Wide Web.
Anirban Mahanti has included themes like Moment, Social network, Instruction prefetch, SIMPLE and PATH in his Multimedia study.
His Popularity research incorporates elements of Quality of experience, Metadata, Early detection, Mobile apps and Focus.
His Repeated game research includes elements of Stackelberg competition and Nash equilibrium.
His Operations research study which covers Simulation that intersects with Distributed generation, Scalability, Subgame perfect equilibrium, Model predictive control and Reduction.
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