What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Programming language
- Algorithm
His primary areas of investigation include High-definition video, Content-addressable memory, Scalability, CMOS and Parallel computing.
His High-definition video study combines topics from a wide range of disciplines, such as Artificial neural network, Binary number and Robustness.
The Robustness study combines topics in areas such as Computer engineering, Kernel and Unconventional computing.
The study incorporates disciplines such as Semiconductor memory, Random access memory, Computation and Interleaved memory, Computing with Memory in addition to Content-addressable memory.
Abbas Rahimi works mostly in the field of CMOS, limiting it down to topics relating to Resistive random-access memory and, in certain cases, Transistor and Simulation, as a part of the same area of interest.
His research in Parallel computing intersects with topics in Non-volatile memory, Computer hardware, Overhead and Floating-point unit.
His most cited work include:
- A fully-synthesizable single-cycle interconnection network for Shared-L1 processor clusters (96 citations)
- Exploring Hyperdimensional Associative Memory (79 citations)
- Resistive configurable associative memory for approximate computing (75 citations)
What are the main themes of his work throughout his whole career to date?
Abbas Rahimi spends much of his time researching Artificial intelligence, Parallel computing, Pattern recognition, Embedded system and Software.
As part of the same scientific family, Abbas Rahimi usually focuses on Artificial intelligence, concentrating on Hyperdimensional computing and intersecting with Gesture recognition and Machine learning.
His Parallel computing research incorporates themes from Floating point, Error detection and correction and Content-addressable memory.
As a member of one scientific family, Abbas Rahimi mostly works in the field of Content-addressable memory, focusing on Hamming distance and, on occasion, Theoretical computer science.
His work carried out in the field of Pattern recognition brings together such families of science as Embedding, Brain–computer interface and Binary number.
The various areas that Abbas Rahimi examines in his Embedded system study include CMOS and Shared memory.
He most often published in these fields:
- Artificial intelligence (28.21%)
- Parallel computing (23.08%)
- Pattern recognition (17.95%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (28.21%)
- Pattern recognition (17.95%)
- Deep learning (5.98%)
In recent papers he was focusing on the following fields of study:
Abbas Rahimi spends much of his time researching Artificial intelligence, Pattern recognition, Deep learning, Convolutional neural network and Artificial neural network.
His research integrates issues of Set and Hyperdimensional computing in his study of Artificial intelligence.
The concepts of his Pattern recognition study are interwoven with issues in Embedding and False positive paradox.
Abbas Rahimi studied Artificial neural network and Bottleneck that intersect with Theoretical computer science, Computer engineering and Software.
His work deals with themes such as Speech recognition and Preprocessor, which intersect with Classifier.
His Discriminative model research incorporates elements of Evolvable hardware and Content-addressable memory.
Between 2019 and 2021, his most popular works were:
- In-memory hyperdimensional computing (25 citations)
- Hyperdimensional Computing for Blind and One-Shot Classification of EEG Error-Related Potentials (20 citations)
- Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Algorithm
Abbas Rahimi mainly focuses on Artificial intelligence, Pattern recognition, Computation, Robustness and Local binary patterns.
His Artificial intelligence study incorporates themes from Speech recognition and Crossbar switch.
His Pattern recognition research includes themes of Probabilistic logic, False positive paradox, Set and Bloom filter.
His Computation research is multidisciplinary, relying on both Artificial neural network, Theoretical computer science, Binary number and Matching.
His study in Robustness is interdisciplinary in nature, drawing from both Machine learning, CMOS, Hyperdimensional computing and Gesture recognition.
His Local binary patterns research overlaps with One-shot learning, Intracranial Electroencephalography, Deep learning, Seizure onset and Feature extraction.
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