What is he best known for?
The fields of study he is best known for:
- Programming language
- Operating system
- Algorithm
His primary scientific interests are in Programming language, Java, Data structure, Benchmark and Pointer.
His research brings together the fields of Parameterized complexity and Programming language.
His research in Java intersects with topics in Android Beam, Thread and Accuracy and precision.
He has researched Data structure in several fields, including Distributed computing, Programmer, Key, Server and Call stack.
His Benchmark research includes themes of Consistency, Real-time computing, Set and Data mining.
His studies deal with areas such as Algorithm, Compiler, Memory safety and Parallel computing as well as Pointer.
His most cited work include:
- Concurrent constraint programming (681 citations)
- Ownership types for safe programming: preventing data races and deadlocks (549 citations)
- Compositional pointer and escape analysis for Java programs (408 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Programming language, Data structure, Theoretical computer science, Algorithm and Set.
His study brings together the fields of Parallel computing and Programming language.
His biological study spans a wide range of topics, including Object, Consistency and Abstract interpretation.
Many of his studies on Theoretical computer science apply to Program analysis as well.
Much of his study explores Set relationship to Benchmark.
His work carried out in the field of Compiler brings together such families of science as Commutative property, Computation, Pointer and Code.
He most often published in these fields:
- Programming language (24.73%)
- Data structure (18.21%)
- Theoretical computer science (16.30%)
What were the highlights of his more recent work (between 2017-2021)?
- Algorithm (14.13%)
- Artificial intelligence (6.52%)
- Software (7.34%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Algorithm, Artificial intelligence, Software, Software engineering and Inference.
His study in Algorithm is interdisciplinary in nature, drawing from both Probability distribution, Process, Artificial neural network, Robustness and Gibbs sampling.
His Code research extends to Software, which is thematically connected.
His Code research integrates issues from Search-based software engineering and Data mining.
His Inference research incorporates elements of Theoretical computer science, Relational database, Database, Probabilistic logic and Coding.
The concepts of his Semantics study are interwoven with issues in Bitmap, Header, Byte, Perl and Data structure.
Between 2017 and 2021, his most popular works were:
- The three pillars of machine programming (28 citations)
- Probabilistic programming with programmable inference (16 citations)
- Bayesian synthesis of probabilistic programs for automatic data modeling (15 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Operating system
- Algorithm
Martin Rinard mainly focuses on Algorithm, Software engineering, Software, Robustness and Inference.
Martin Rinard works mostly in the field of Algorithm, limiting it down to concerns involving Artificial neural network and, occasionally, Correctness, Floating point and State.
Martin Rinard interconnects Software maintenance, Categorical variable and Code in the investigation of issues within Software engineering.
His Software study incorporates themes from Boilerplate code, Active learning and SIMPLE.
His Inference study combines topics from a wide range of disciplines, such as Relational database, Database, Computation and Coding.
His study focuses on the intersection of Theoretical computer science and fields such as Expression with connections in the field of Probabilistic logic.
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