What is he best known for?
The fields of study he is best known for:
- Operating system
- Artificial intelligence
- Programming language
The scientist’s investigation covers issues in Artificial intelligence, Parallel computing, Machine learning, Compiler and Feature extraction.
The concepts of his Artificial intelligence study are interwoven with issues in Computer security, Threat model and Computation.
His Parallel computing research is multidisciplinary, incorporating perspectives in Scheduling and Kernel.
In his study, Instruction-level parallelism, Software deployment and Data modeling is inextricably linked to Optimizing compiler, which falls within the broad field of Machine learning.
His Compiler research is multidisciplinary, incorporating elements of Xeon, Shared memory, Multiprocessing, Data parallelism and Multi-core processor.
His research in Feature extraction intersects with topics in Variety and Feature.
His most cited work include:
- Towards a holistic approach to auto-parallelization: integrating profile-driven parallelism detection and machine-learning based mapping (203 citations)
- Mapping parallelism to multi-cores: a machine learning based approach (172 citations)
- Portable mapping of data parallel programs to OpenCL for heterogeneous systems (104 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Parallel computing, Machine learning, Compiler and Speedup.
His work deals with themes such as Program optimization, Software portability and Kernel, which intersect with Parallel computing.
His work in Machine learning addresses issues such as Heuristics, which are connected to fields such as Heuristic and Active learning.
His Compiler study combines topics in areas such as Instruction-level parallelism, Shared memory and Benchmark.
His study in Speedup is interdisciplinary in nature, drawing from both Software, Task parallelism and Partition.
His Task parallelism research focuses on subjects like Data parallelism, which are linked to Xeon.
He most often published in these fields:
- Artificial intelligence (29.79%)
- Parallel computing (19.15%)
- Machine learning (18.44%)
What were the highlights of his more recent work (between 2019-2021)?
- Parallel computing (19.15%)
- Artificial intelligence (29.79%)
- Program optimization (4.26%)
In recent papers he was focusing on the following fields of study:
Zheng Wang focuses on Parallel computing, Artificial intelligence, Program optimization, Overhead and Speedup.
His Parallel computing study combines topics from a wide range of disciplines, such as Memory footprint, Filter and Row.
His Artificial intelligence research incorporates themes from Java and Machine learning.
The Program optimization study combines topics in areas such as Symmetric multiprocessor system, Software portability, Variety, Overhead and Hardware architecture.
Zheng Wang has included themes like Scheme, Iterative method, Optimization problem and Shared memory in his Overhead study.
His studies in Speedup integrate themes in fields like Compiler, Greedy algorithm, Image tracing, Partition and SIMD.
Between 2019 and 2021, his most popular works were:
- Optimizing Deep Learning Inference on Embedded Systems Through Adaptive Model Selection (16 citations)
- Neighborhood Matching Network for Entity Alignment (8 citations)
- Characterizing Scalability of Sparse Matrix–Vector Multiplications on Phytium FT-2000+ (6 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Artificial intelligence
- Programming language
His primary areas of study are Program optimization, Artificial intelligence, Deep learning, Parallel computing and Distributed computing.
His research integrates issues of Java and Reduction in his study of Artificial intelligence.
His work carried out in the field of Deep learning brings together such families of science as Recurrent neural network, Graph, Data compression, Embedded system and Machine translation.
Zheng Wang combines subjects such as Variety, Software portability and Overhead with his study of Parallel computing.
His Distributed computing research focuses on Partition and how it relates to Speedup.
He focuses mostly in the field of Multi-core processor, narrowing it down to topics relating to Software and, in certain cases, Source code, Code, Machine learning and Data modeling.
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