What is he best known for?
The fields of study he is best known for:
- Statistics
- Artificial intelligence
- Machine learning
Sanjeev R. Kulkarni mainly focuses on Artificial intelligence, Algorithm, Machine learning, Independent and identically distributed random variables and Discrete mathematics.
The Artificial intelligence study combines topics in areas such as Stability, Probability distribution, Computer vision and Pattern recognition.
The concepts of his Algorithm study are interwoven with issues in Upper and lower bounds and Linear system.
Sanjeev R. Kulkarni has researched Machine learning in several fields, including Wireless sensor network and Probabilistic logic.
Sanjeev R. Kulkarni interconnects Deterministic algorithm, k-nearest neighbors algorithm, Estimator, Divergence and Rate of convergence in the investigation of issues within Independent and identically distributed random variables.
His Discrete mathematics study combines topics from a wide range of disciplines, such as Entropy, Information theory, Entropy rate and Pure mathematics.
His most cited work include:
- A deterministic approach to throughput scaling in wireless networks (333 citations)
- Divergence Estimation for Multidimensional Densities Via $k$ -Nearest-Neighbor Distances (257 citations)
- Upper bounds to transport capacity of wireless networks (250 citations)
What are the main themes of his work throughout his whole career to date?
Sanjeev R. Kulkarni spends much of his time researching Artificial intelligence, Algorithm, Discrete mathematics, Mathematical optimization and Upper and lower bounds.
His study looks at the intersection of Artificial intelligence and topics like Machine learning with Wireless sensor network.
His biological study deals with issues like Estimator, which deal with fields such as Applied mathematics and k-nearest neighbors algorithm.
His Discrete mathematics research is multidisciplinary, relying on both Function, Entropy rate, Combinatorics and Low-density parity-check code.
His research integrates issues of Stochastic process and Estimation theory in his study of Mathematical optimization.
His Upper and lower bounds research incorporates themes from Wireless network, Random walk, Markov chain and Minimax.
He most often published in these fields:
- Artificial intelligence (21.60%)
- Algorithm (19.14%)
- Discrete mathematics (12.04%)
What were the highlights of his more recent work (between 2011-2021)?
- Artificial intelligence (21.60%)
- Data mining (6.79%)
- Query language (3.40%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Data mining, Query language, Upper and lower bounds and Wireless.
His biological study spans a wide range of topics, including Group method of data handling, Machine learning, Computer vision, Algorithm and Pattern recognition.
His study on Upper and lower bounds also encompasses disciplines like
- Markov chain, which have a strong connection to Discrete mathematics,
- Random walk that intertwine with fields like Graph.
His Discrete mathematics research is multidisciplinary, incorporating perspectives in Node, Convergence, Asynchronous communication and Combinatorics.
His Wireless research includes themes of Computer network and Fading.
Sanjeev R. Kulkarni has included themes like Communication channel and Independent and identically distributed random variables in his Computer network study.
Between 2011 and 2021, his most popular works were:
- Machine Learning Methods for Attack Detection in the Smart Grid (230 citations)
- TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking. (115 citations)
- Sparse Attack Construction and State Estimation in the Smart Grid: Centralized and Distributed Models (106 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Data mining, Distributed computing, Recommender system and Smart grid.
Sanjeev R. Kulkarni combines subjects such as Machine learning, Computer vision and Pattern recognition with his study of Artificial intelligence.
His Machine learning research integrates issues from Gaze, Crowdsourcing, Tracking system, Eye tracking and Salience.
His work on Anomaly detection, Big data and Query language as part of general Data mining study is frequently connected to Complex event processing, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Distributed computing study combines topics in areas such as Radio spectrum, Reliability, Broadcasting and Stochastic game.
His studies deal with areas such as Spectral clustering, Cluster analysis and Graph as well as Recommender system.
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