What is he best known for?
The fields of study Vaneet Aggarwal is best known for:
- Algorithm
- Sampling (signal processing)
- Computer network
His work on Microeconomics as part of general Profit (economics) research is frequently linked to Operations research, thereby connecting diverse disciplines of science.
While working in this field, Vaneet Aggarwal studies both Microeconomics and Profit (economics).
The Quantum mechanics research Vaneet Aggarwal does as part of his general Gaussian study is frequently linked to other disciplines of science, such as Combinatorics, therefore creating a link between diverse domains of science.
His research on Quantum mechanics frequently connects to adjacent areas such as Gaussian.
He integrates many fields in his works, including Telecommunications and Wireless.
In his work, Vaneet Aggarwal performs multidisciplinary research in Wireless and Telecommunications.
Algorithm is closely attributed to Erasure code in his study.
Many of his studies involve connections with topics such as Decoding methods and Erasure code.
Many of his studies on Decoding methods apply to Algorithm as well.
His most cited work include:
- DeepPool: Distributed Model-Free Algorithm for Ride-Sharing Using Deep Reinforcement Learning (87 citations)
- Joint Latency and Cost Optimization for Erasure-Coded Data Center Storage (84 citations)
- From Federated to Fog Learning: Distributed Machine Learning over Heterogeneous Wireless Networks (67 citations)
What are the main themes of his work throughout his whole career to date
His work on Telecommunications as part of general Decoding methods research is frequently linked to Programming language, thereby connecting diverse disciplines of science.
Vaneet Aggarwal links adjacent fields of study such as Channel (broadcasting), Wireless, Decoding methods and Transmission (telecommunications) in the subject of Telecommunications.
He performs multidisciplinary studies into Computer network and Real-time computing in his work.
In his articles, Vaneet Aggarwal combines various disciplines, including Real-time computing and Computer network.
He conducted interdisciplinary study in his works that combined Algorithm and Artificial intelligence.
He undertakes interdisciplinary study in the fields of Artificial intelligence and Algorithm through his research.
In his research, Vaneet Aggarwal undertakes multidisciplinary study on Operating system and Cloud computing.
Vaneet Aggarwal applies his multidisciplinary studies on Distributed computing and Operating system in his research.
Vaneet Aggarwal incorporates Statistics and Mathematical optimization in his studies.
Vaneet Aggarwal most often published in these fields:
- Computer network (44.05%)
- Telecommunications (42.86%)
- Algorithm (39.29%)
What were the highlights of his more recent work (between 2020-2022)?
- Artificial intelligence (88.89%)
- Statistics (33.33%)
- Reinforcement learning (33.33%)
In recent works Vaneet Aggarwal was focusing on the following fields of study:
In most of his Statistics studies, his work intersects topics such as Matching (statistics) and Flexibility (engineering).
His Flexibility (engineering) study frequently draws connections between related disciplines such as Statistics.
His Combinatorics study frequently draws connections to other fields, such as Topology (electrical circuits) and Dimension (graph theory).
His Topology (electrical circuits) study frequently links to adjacent areas such as Combinatorics.
His study deals with a combination of Artificial intelligence and Human–computer interaction.
The study of Operating system is intertwined with the study of Heuristics in a number of ways.
He performs integrative Transport engineering and Traffic congestion research in his work.
Vaneet Aggarwal merges many fields, such as Traffic congestion and Transport engineering, in his writings.
His research ties Software-defined networking and Distributed computing together.
Between 2020 and 2022, his most popular works were:
- Fault-Resilience for Bandwidth Management in Industrial Software-Defined Networks (35 citations)
- FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers and Goods Transportation (31 citations)
- A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching Using Deep Reinforcement Learning (21 citations)
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