What is he best known for?
The fields of study he is best known for:
- Computer network
- Programming language
- Artificial intelligence
The scientist’s investigation covers issues in Computer network, Real-time computing, Simulation, Scalability and Wireless network.
Many of his research projects under Computer network are closely connected to Network interface with Network interface, tying the diverse disciplines of science together.
His work carried out in the field of Real-time computing brings together such families of science as Time division multiple access, Link layer, Wireless sensor network, Enhanced Data Rates for GSM Evolution and Swarm behaviour.
His research integrates issues of Systems design, Vehicular ad hoc network and Traffic congestion in his study of Simulation.
His studies deal with areas such as Model predictive control, Data-driven, Random forest, Demand response and Robustness as well as Scalability.
He has included themes like Minimum energy control, Quality of service, Resource allocation and Communications system in his Wireless network study.
His most cited work include:
- The Wireless Control Network: A New Approach for Control Over Networks (150 citations)
- RT-Link: A Time-Synchronized Link Protocol for Energy- Constrained Multi-hop Wireless Networks (141 citations)
- Cyber–Physical Modeling of Implantable Cardiac Medical Devices (116 citations)
What are the main themes of his work throughout his whole career to date?
Rahul Mangharam focuses on Embedded system, Distributed computing, Wireless network, Real-time computing and Computer network.
His Embedded system research includes themes of Software and Formal verification.
His Distributed computing research incorporates themes from Network topology, Process control and Collision avoidance.
His work focuses on many connections between Wireless network and other disciplines, such as Wireless sensor network, that overlap with his field of interest in Node, Wireless and Virtual machine.
His biological study deals with issues like Scheduling, which deal with fields such as Peak demand.
His Computer network study which covers Scalability that intersects with Robustness.
He most often published in these fields:
- Embedded system (15.00%)
- Distributed computing (13.89%)
- Wireless network (13.89%)
What were the highlights of his more recent work (between 2017-2021)?
- Robustness (9.44%)
- Temporal logic (6.11%)
- Model predictive control (9.44%)
In recent papers he was focusing on the following fields of study:
Rahul Mangharam spends much of his time researching Robustness, Temporal logic, Model predictive control, Mathematical optimization and Scalability.
His Model predictive control study combines topics in areas such as Control engineering and State.
The Mathematical optimization study which covers Data-driven that intersects with Random forest and Demand response.
The Demand response study combines topics in areas such as Energy management, Physical plant, Reduction, System dynamics and Cyber-physical system.
His Scalability study combines topics from a wide range of disciplines, such as Separation, Distributed computing and Collision avoidance.
His research combines Linear programming and Distributed computing.
Between 2017 and 2021, his most popular works were:
- Data-driven model predictive control using random forests for building energy optimization and climate control (87 citations)
- Learning and control using gaussian processes: towards bridging machine learning and controls for physical systems (39 citations)
- Data-Driven Model Predictive Control with Regression Trees—An Application to Building Energy Management (33 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Programming language
- Artificial intelligence
His primary areas of study are Model predictive control, Mathematical optimization, Data-driven, Robustness and Random forest.
Mathematical optimization is frequently linked to Demand response in his study.
He interconnects Energy management, Physical plant, Reduction, System dynamics and Cyber-physical system in the investigation of issues within Demand response.
His biological study spans a wide range of topics, including Scalability, Building model and Temporal logic.
His studies in Scalability integrate themes in fields like Control system, Logic Control, Distributed computing and Signal temporal logic.
His work in Random forest covers topics such as Benchmark which are related to areas like Optimal control, Process control, Energy management system and Time horizon.
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