What is he best known for?
The fields of study he is best known for:
- Electrical engineering
- Artificial intelligence
- Control theory
His primary areas of study are Phase-change memory, Electronic engineering, Nanotechnology, Neuromorphic engineering and Spiking neural network.
In his articles, Abu Sebastian combines various disciplines, including Phase-change memory and Key.
His research integrates issues of Transfer function, Actuator, Electrical engineering, Microelectromechanical systems and Reliability in his study of Electronic engineering.
His work on Nanoscopic scale as part of general Nanotechnology research is often related to Stochastic process, thus linking different fields of science.
His Neuromorphic engineering research focuses on subjects like Crossbar switch, which are linked to Path, Signal and Massively parallel.
His study looks at the intersection of Spiking neural network and topics like Computer architecture with State.
His most cited work include:
- Stochastic phase-change neurons (433 citations)
- Neuromorphic computing using non-volatile memory (393 citations)
- High bandwidth nano-positioner: A robust control approach (379 citations)
What are the main themes of his work throughout his whole career to date?
Abu Sebastian focuses on Electronic engineering, Phase-change memory, Resistive random-access memory, Artificial intelligence and Electrical engineering.
Abu Sebastian has researched Electronic engineering in several fields, including Cantilever, Nonlinear system, Computer data storage and Microelectromechanical systems.
His Cantilever study combines topics in areas such as Nanotechnology and Actuator.
Abu Sebastian focuses mostly in the field of Phase-change memory, narrowing it down to topics relating to Artificial neural network and, in certain cases, Crossbar switch.
His work deals with themes such as Signal, State and Topology, which intersect with Resistive random-access memory.
His work on Deep learning, Spiking neural network and Inference as part of general Artificial intelligence research is frequently linked to In-Memory Processing, thereby connecting diverse disciplines of science.
He most often published in these fields:
- Electronic engineering (26.74%)
- Phase-change memory (20.54%)
- Resistive random-access memory (15.12%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (12.79%)
- In-Memory Processing (8.53%)
- Deep learning (6.98%)
In recent papers he was focusing on the following fields of study:
Abu Sebastian mainly focuses on Artificial intelligence, In-Memory Processing, Deep learning, Phase-change memory and Artificial neural network.
His study looks at the relationship between Artificial intelligence and topics such as Crossbar switch, which overlap with Compensation and Nonlinear system.
He interconnects Computer architecture, Computer engineering and Analog computer in the investigation of issues within Deep learning.
His Phase-change memory research integrates issues from Resistive random-access memory, Electronic engineering, Mixing, Convolutional neural network and Electrical engineering.
His research in Electronic engineering is mostly focused on Bandwidth.
His studies in Artificial neural network integrate themes in fields like Pipeline, Computation, Dataflow and Pattern recognition.
Between 2018 and 2021, his most popular works were:
- Memory devices and applications for in-memory computing. (103 citations)
- In-memory computing on a photonic platform (87 citations)
- Accurate deep neural network inference using computational phase-change memory. (33 citations)
In his most recent research, the most cited papers focused on:
- Electrical engineering
- Artificial intelligence
- Control theory
His main research concerns Artificial intelligence, Phase-change memory, Deep learning, In-Memory Processing and Von Neumann architecture.
His work in the fields of Artificial intelligence, such as Artificial neural network, Spiking neural network and Robustness, intersects with other areas such as Randomness.
His Spiking neural network study integrates concerns from other disciplines, such as Neuromorphic engineering, Supervised learning, Spike and Computational model.
His research investigates the connection with Neuromorphic engineering and areas like Computer architecture which intersect with concerns in Computation.
His Phase-change memory study incorporates themes from Convolutional neural network, Electrical engineering and Resistive random-access memory.
His In-Memory Processing research overlaps with other disciplines such as Data processing, Computer data storage and Electronic circuit.
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.
