What is he best known for?
The fields of study he is best known for:
- Programming language
- Artificial intelligence
- Object-oriented programming
His primary areas of investigation include Programming language, Data mining, Temporal database, Software engineering and Theoretical computer science.
His Programming language research is multidisciplinary, relying on both Generator and Artificial intelligence.
His studies in Data mining integrate themes in fields like Inference, Type, Set and Knowledge representation and reasoning.
Martin Erwig works mostly in the field of Temporal database, limiting it down to topics relating to Abstract data type and, in certain cases, Data type, as a part of the same area of interest.
His work in the fields of Software engineering, such as System testing, intersects with other areas such as Test design.
He has included themes like Butterfly graph, Graph property, Graph rewriting, Complement graph and Outerplanar graph in his Theoretical computer science study.
His most cited work include:
- A foundation for representing and querying moving objects (673 citations)
- The state of the art in end-user software engineering (393 citations)
- Spatio-Temporal Data Types: An Approach to Modeling and Querying Moving Objects in Databases (349 citations)
What are the main themes of his work throughout his whole career to date?
Martin Erwig mainly investigates Programming language, Theoretical computer science, Haskell, Artificial intelligence and Data mining.
His biological study spans a wide range of topics, including Type and Software engineering.
His Theoretical computer science research incorporates elements of Data type, Query language, Line graph, Algorithm and Reasoning system.
He works mostly in the field of Artificial intelligence, limiting it down to concerns involving Natural language processing and, occasionally, Visual language.
Martin Erwig combines subjects such as Inference and Set with his study of Data mining.
His Temporal database study integrates concerns from other disciplines, such as Data modeling, Embedding, Object and Abstract data type.
He most often published in these fields:
- Programming language (47.93%)
- Theoretical computer science (23.08%)
- Haskell (17.16%)
What were the highlights of his more recent work (between 2011-2021)?
- Programming language (47.93%)
- Theoretical computer science (23.08%)
- Artificial intelligence (14.79%)
In recent papers he was focusing on the following fields of study:
His main research concerns Programming language, Theoretical computer science, Artificial intelligence, Haskell and Type inference.
His work on Gradual typing as part of general Programming language research is frequently linked to Typing, thereby connecting diverse disciplines of science.
His Theoretical computer science study incorporates themes from Data type, Software and Notation.
His studies in Data type integrate themes in fields like Mathematical optimization and Algebra.
The various areas that Martin Erwig examines in his Artificial intelligence study include Natural language processing, Field, Visual language, Semantics and Machine learning.
His Type inference study also includes
- Process which connect with Set, Computation, Data mining and Expression,
- Expression which is related to area like Unification, Lambda calculus, Principal type and Range,
- Debugging which intersects with area such as Identification and Task.
Between 2011 and 2021, his most popular works were:
- Extending Type Inference to Variational Programs (52 citations)
- Counter-factual typing for debugging type errors (45 citations)
- An error-tolerant type system for variational lambda calculus (32 citations)
In his most recent research, the most cited papers focused on:
- Programming language
- Artificial intelligence
- Software
His scientific interests lie mostly in Programming language, Type inference, Theoretical computer science, Expression and Set.
His work in the fields of Programming language, such as White-box testing, Symbolic execution and Domain, overlaps with other areas such as Model-based testing and Random testing.
His study on Type inference also encompasses disciplines like
- Debugging which connect with Programmer, Computation and Data mining,
- Process which is related to area like Software development.
His Theoretical computer science research integrates issues from Data type, Software and Variation.
His biological study spans a wide range of topics, including Algorithm, Lambda calculus and Unification.
His work deals with themes such as Consistency, Relational model and Model inference, which intersect with Set.
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