What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Computer network
Wireless sensor network, Distributed computing, Computer network, Wireless and Key distribution in wireless sensor networks are his primary areas of study.
The study incorporates disciplines such as Energy consumption, Energy harvesting, Sensor node, Real-time computing and Mobile wireless sensor network in addition to Wireless sensor network.
Mani Srivastava has included themes like Overhead, Wireless ad hoc network, Link-state routing protocol, Static routing and Dynamic Source Routing in his Distributed computing study.
His Computer network research incorporates elements of Wireless network, Sensor array and Wireless WAN.
Mani Srivastava combines subjects such as Software deployment, Quality of service, Communication channel and Algorithm, Optimization problem with his study of Wireless.
His studies deal with areas such as Security service and Communications protocol as well as Key distribution in wireless sensor networks.
His most cited work include:
- Timing-sync protocol for sensor networks (1920 citations)
- Coverage problems in wireless ad-hoc sensor networks (1636 citations)
- Energy-aware wireless microsensor networks (1532 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Wireless sensor network, Computer network, Real-time computing, Distributed computing and Wireless.
His studies in Wireless sensor network integrate themes in fields like Energy consumption, Sensor node, Key distribution in wireless sensor networks, Embedded system and Node.
His Computer network research includes themes of Wireless network and Wireless ad hoc network.
The various areas that Mani Srivastava examines in his Real-time computing study include Exploit, Scheduling and Energy harvesting.
The concepts of his Distributed computing study are interwoven with issues in Scalability and Software deployment.
His Wireless study incorporates themes from Power management and Communication channel.
He most often published in these fields:
- Wireless sensor network (35.43%)
- Computer network (19.65%)
- Real-time computing (17.87%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (8.37%)
- Deep learning (2.74%)
- Machine learning (2.90%)
In recent papers he was focusing on the following fields of study:
Mani Srivastava mostly deals with Artificial intelligence, Deep learning, Machine learning, Real-time computing and Artificial neural network.
His research investigates the connection between Artificial intelligence and topics such as Pattern recognition that intersect with problems in Image.
His work carried out in the field of Deep learning brings together such families of science as Interpretability, Information privacy and Data mining.
His Real-time computing research is multidisciplinary, incorporating elements of Wireless, Extended Kalman filter, Wireless sensor network, Inertial measurement unit and Reinforcement learning.
The Extended Kalman filter study combines topics in areas such as Time synchronization and Distributed computing.
Much of his study explores Distributed computing relationship to Server.
Between 2016 and 2021, his most popular works were:
- Generating Natural Language Adversarial Examples (243 citations)
- Accelerating Binarized Convolutional Neural Networks with Software-Programmable FPGAs (233 citations)
- Interpretability of deep learning models: A survey of results (119 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Computer network
His primary scientific interests are in Artificial intelligence, Machine learning, Deep learning, Adversarial system and Artificial neural network.
His Artificial intelligence research incorporates elements of Field-programmable gate array and Smartwatch, Wearable computer.
His Machine learning research includes elements of Data modeling, Inference, Process and Accelerometer.
His work is dedicated to discovering how Deep learning, Data mining are connected with Cloud computing, Information privacy and Synthetic data and other disciplines.
His Adversarial system research focuses on subjects like Sentiment analysis, which are linked to Word and Robustness.
His research in Software focuses on subjects like Scalability, which are connected to Embedded system.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
