What is he best known for?
The fields of study he is best known for:
- Computer network
- Operating system
- The Internet
His primary scientific interests are in Smart grid, Distributed computing, Computer network, Artificial intelligence and Packet switching.
His work deals with themes such as Electric power system, Telecommunications network, Network topology, Electrification and Simulation, which intersect with Smart grid.
His study in Distributed computing is interdisciplinary in nature, drawing from both Static routing, Virtualization and Provisioning.
The concepts of his Computer network study are interwoven with issues in Optical networking, Wavelength-division multiplexing and Optical Transport Network.
His research investigates the connection with Artificial intelligence and areas like Trajectory which intersect with concerns in Statistical classification, Data stream and Pattern recognition.
The Packet switching study combines topics in areas such as Optical burst switching, Electronic engineering, Bandwidth, Reconfigurability and Circuit switching.
His most cited work include:
- Optimizing smart energy control strategies for plug-in hybrid electric vehicle charging (260 citations)
- Worldwide energy needs for ICT: The rise of power-aware networking (255 citations)
- The European IST project DAVID: a viable approach toward optical packet switching (205 citations)
What are the main themes of his work throughout his whole career to date?
Chris Develder spends much of his time researching Computer network, Distributed computing, Smart grid, Cloud computing and Anycast.
His Computer network research integrates issues from Scalability and Optical burst switching.
His biological study deals with issues like Efficient energy use, which deal with fields such as Energy consumption.
His study looks at the relationship between Smart grid and topics such as Wind power, which overlap with Automotive engineering.
His work carried out in the field of Cloud computing brings together such families of science as Virtual network and Data center.
His Anycast study combines topics in areas such as Integer programming and Backup.
He most often published in these fields:
- Computer network (39.36%)
- Distributed computing (23.29%)
- Smart grid (12.85%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (9.64%)
- Artificial neural network (3.21%)
- Relationship extraction (4.42%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Artificial neural network, Relationship extraction, Data mining and Electric vehicle.
Chris Develder has included themes like Machine learning, Pattern recognition, Trajectory and Natural language processing in his Artificial intelligence study.
His Data mining study combines topics from a wide range of disciplines, such as Real-time computing, Base load power plant, STREAMS and Voltage.
His Real-time computing research is multidisciplinary, relying on both Wireless and Energy consumption.
The study incorporates disciplines such as Markov decision process, Demand response, Smart grid and Reinforcement learning in addition to Electric vehicle.
His studies deal with areas such as Simulation and Synthetic data as well as Smart grid.
Between 2016 and 2021, his most popular works were:
- Joint entity recognition and relation extraction as a multi-head selection problem (97 citations)
- Adversarial training for multi-context joint entity and relation extraction (63 citations)
- Appliance classification using VI trajectories and convolutional neural networks (63 citations)
In his most recent research, the most cited papers focused on:
- Computer network
- Operating system
- Artificial intelligence
Chris Develder mostly deals with Artificial intelligence, Machine learning, Relationship extraction, Data mining and Artificial neural network.
He works mostly in the field of Artificial intelligence, limiting it down to topics relating to Trajectory and, in certain cases, Pattern recognition.
His work focuses on many connections between Relationship extraction and other disciplines, such as Parsing, that overlap with his field of interest in Feature, Sequence labeling, Selection, Dependency and Margin.
He interconnects Grid connection, Voltage and Feature in the investigation of issues within Data mining.
His research integrates issues of Adversarial system, Regularization and Training set in his study of Artificial neural network.
His Cluster analysis research incorporates themes from Exploit, Data-driven, Simulation and Smart grid.
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