What is he best known for?
The fields of study he is best known for:
- Programming language
- Operating system
- Object-oriented programming
His primary areas of investigation include Programming language, Theoretical computer science, Java, Object-oriented programming and Constant.
His study in Predicate dispatch, Double dispatch, Multiple dispatch, Pointer and Optimizing compiler falls within the category of Programming language.
His study in Theoretical computer science is interdisciplinary in nature, drawing from both Compiler, Soundness, Range, Algorithm and Network configuration.
As a part of the same scientific family, Todd Millstein mostly works in the field of Java, focusing on Class and, on occasion, Syntax, Inheritance, Object and Semantics.
His Object-oriented programming research integrates issues from Java annotation and strictfp.
His Constant research is multidisciplinary, incorporating perspectives in Web application, String and Software bug.
His most cited work include:
- Automatic predicate abstraction of C programs (738 citations)
- MultiJava: modular open classes and symmetric multiple dispatch for Java (277 citations)
- RERAN: timing- and touch-sensitive record and replay for Android (214 citations)
What are the main themes of his work throughout his whole career to date?
Todd Millstein mainly investigates Programming language, Theoretical computer science, Compiler, Java and Inference.
Programming language connects with themes related to Memory model in his study.
His Theoretical computer science research includes themes of Soundness, Parameterized complexity, Rule-based machine translation and Implementation.
His work investigates the relationship between Compiler and topics such as Domain-specific language that intersect with problems in Fault tolerance.
His Java research incorporates themes from Object and Code.
His work deals with themes such as Data-driven, Probabilistic logic, Key and Loop invariant, which intersect with Inference.
He most often published in these fields:
- Programming language (44.62%)
- Theoretical computer science (21.54%)
- Compiler (16.92%)
What were the highlights of his more recent work (between 2018-2021)?
- Inference (11.54%)
- Theoretical computer science (21.54%)
- Algorithm (6.15%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Inference, Theoretical computer science, Algorithm, Implementation and Inductive synthesis.
His Inference study combines topics from a wide range of disciplines, such as Probabilistic logic and Key.
His work focuses on many connections between Probabilistic logic and other disciplines, such as Denotational semantics, that overlap with his field of interest in Logical reasoning.
His Theoretical computer science research incorporates elements of Data-driven, Compiler, Representation and Artifact.
Within one scientific family, Todd Millstein focuses on topics pertaining to Abstract type under Data-driven, and may sometimes address concerns connected to Recursion, Abstract data type, Code and Representation.
His Implementation study contributes to a more complete understanding of Programming language.
Between 2018 and 2021, his most popular works were:
- Scaling exact inference for discrete probabilistic programs (9 citations)
- Overfitting in Synthesis: Theory and Practice (6 citations)
- Dice: Compiling Discrete Probabilistic Programs for Scalable Inference. (5 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Operating system
- Object-oriented programming
His main research concerns Theoretical computer science, Probabilistic logic, Inference, Implementation and Inductive synthesis.
His study in the fields of Probabilistic inference and Probabilistic programming language under the domain of Probabilistic logic overlaps with other disciplines such as Dice and Power graph analysis.
His Probabilistic inference study incorporates themes from Compiler, Approximate inference, Leverage, Logical reasoning and Bayesian network.
His Inference investigation overlaps with other disciplines such as Random variable and Probability distribution.
Implementation is a subfield of Programming language that he investigates.
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