What is he best known for?
The fields of study he is best known for:
- Programming language
- Operating system
- Artificial intelligence
His main research concerns Model checking, Theoretical computer science, Algorithm, Boolean satisfiability problem and Formal verification.
His Model checking study introduces a deeper knowledge of Programming language.
His work in the fields of Software, Sequential consistency, Witness and Programmer overlaps with other areas such as Bounded function.
His Theoretical computer science research includes elements of Boolean circuit and And-inverter graph.
In most of his Algorithm studies, his work intersects topics such as Representation.
His Boolean satisfiability problem study combines topics from a wide range of disciplines, such as #SAT, Set, Computability and Heuristics.
His most cited work include:
- System and method for modeling, abstraction, and analysis of software (203 citations)
- Iterative Abstraction using SAT-based BMC with Proof Analysis (116 citations)
- F-SOFT: software verification platform (103 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Theoretical computer science, Software, Model checking, Programming language and Algorithm.
His biological study spans a wide range of topics, including Path, Set and Heuristics.
His Software research also works with subjects such as
- Java together with Code and Escape analysis,
- Data mining which intersects with area such as Artificial intelligence.
His work on Symbolic trajectory evaluation is typically connected to Bounded function as part of general Model checking study, connecting several disciplines of science.
In his study, which falls under the umbrella issue of Programming language, Debugging and Satisfiability modulo theories is strongly linked to Symbolic data analysis.
His Algorithm study frequently involves adjacent topics like Representation.
He most often published in these fields:
- Theoretical computer science (23.97%)
- Software (23.29%)
- Model checking (21.23%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (11.64%)
- Software (23.29%)
- Machine learning (8.90%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Software, Machine learning, Scalability and Deep learning.
His Artificial intelligence research integrates issues from Root cause, Scheduling and Fuzz testing.
His Software research includes themes of Redundancy, Information retrieval and Java.
His Scalability research is multidisciplinary, incorporating perspectives in Transfer of learning, Privilege, Bayesian optimization and Time complexity.
His study in Graph is interdisciplinary in nature, drawing from both Theoretical computer science and Node.
System call is a primary field of his research addressed under Programming language.
Between 2017 and 2021, his most popular works were:
- Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets (91 citations)
- Microstructural Materials Design Via Deep Adversarial Learning Methodology (71 citations)
- Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches (46 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Operating system
- Artificial intelligence
Scalability, Software, Artificial intelligence, Deep learning and Symbolic execution are his primary areas of study.
His Software study incorporates themes from Redundancy, Instruction set, Theoretical computer science and Java.
Zijiang Yang combines subjects such as Software system, Layer and Heuristics with his study of Theoretical computer science.
In his research, Context, Feature extraction and Software bug is intimately related to Machine learning, which falls under the overarching field of Artificial intelligence.
His work carried out in the field of Deep learning brings together such families of science as Transfer of learning, Bayesian optimization and Convolutional neural network.
The various areas that Zijiang Yang examines in his Symbolic execution study include Computer security, Rank and Smart contract.
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