What is he best known for?
The fields of study he is best known for:
- Computer network
- Statistics
- The Internet
Sanjay Shakkottai mainly focuses on Computer network, Scheduling, Wireless network, Wireless and Distributed computing.
The various areas that he examines in his Computer network study include Wireless ad hoc network, Transmission, Throughput and Communication channel.
His research integrates issues of Queue, Code division multiple access, Real-time computing and Network packet in his study of Scheduling.
His Network packet study combines topics in areas such as Wireless lan and Radio resource management.
In his study, which falls under the umbrella issue of Wireless network, Linear network coding, Multicast and Latency is strongly linked to Mathematical optimization.
His Wireless study integrates concerns from other disciplines, such as Broadband, The Internet, Random variable and Base station.
His most cited work include:
- Cross-layer design for wireless networks (815 citations)
- Unreliable sensor grids: coverage, connectivity and diameter (506 citations)
- Scheduling algorithms for a mixture of real-time and non-real-time data in HDR (393 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Computer network, Scheduling, Wireless network, Queue and Mathematical optimization.
His study in Computer network focuses on Network packet in particular.
His Scheduling research is multidisciplinary, incorporating perspectives in Quality of service, Telecommunications link and Communication channel.
His Wireless network research is multidisciplinary, incorporating elements of Orthogonal frequency-division multiplexing, Wireless sensor network, Lyapunov function and Base station.
His Queue research integrates issues from Real-time computing, Queueing theory, Server and Rate function.
The concepts of his Mathematical optimization study are interwoven with issues in Regret, Markov process and Markov chain.
He most often published in these fields:
- Computer network (35.97%)
- Scheduling (28.06%)
- Wireless network (22.53%)
What were the highlights of his more recent work (between 2018-2021)?
- Regret (13.83%)
- Upper and lower bounds (10.67%)
- Algorithm (14.62%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Regret, Upper and lower bounds, Algorithm, Combinatorics and Mathematical optimization.
Sanjay Shakkottai combines subjects such as Subspace topology, Theoretical computer science, Pairwise comparison and Markov chain with his study of Regret.
Sanjay Shakkottai has included themes like Exponential family and Independent and identically distributed random variables in his Algorithm study.
The study incorporates disciplines such as Dimension, Scheduling and Smoothness in addition to Mathematical optimization.
Sanjay Shakkottai interconnects Wireless network, Queueing theory and Queue in the investigation of issues within Scheduling.
His research in Wireless network focuses on subjects like Base station, which are connected to Lyapunov function.
Between 2018 and 2021, his most popular works were:
- Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks (32 citations)
- Social Learning in Multi Agent Multi Armed Bandits (15 citations)
- The Gossiping-Insert-Eliminate Algorithm for Multi-Agent Bandits (10 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Statistics
- The Internet
His main research concerns Regret, Upper and lower bounds, Stochastic approximation, Lyapunov function and Applied mathematics.
His studies deal with areas such as Algorithm, Stochastic modelling, Pairwise comparison and Statistical parameter as well as Regret.
His Algorithm research includes elements of Multi-armed bandit and Fidelity.
Sanjay Shakkottai has researched Upper and lower bounds in several fields, including Discrete mathematics and Regression.
His Stochastic approximation research incorporates elements of Norm, Rate of convergence, Iterated function and Reinforcement learning.
His Lyapunov function research includes themes of Q-learning, Markov process, Asynchronous communication and Temporal difference learning.
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