What is he best known for?
The fields of study he is best known for:
- Programming language
- Artificial intelligence
- Machine learning
Josef Urban mostly deals with Programming language, Mizar system, Mathematical proof, Automated reasoning and Automated theorem proving.
Programming language is frequently linked to Proof assistant in his study.
His Mizar system study incorporates themes from Machine learning and XML.
The various areas that Josef Urban examines in his Mathematical proof study include Axiom, HOL and Theoretical computer science.
His study in Automated reasoning is interdisciplinary in nature, drawing from both Automated proof checking, Formal proof and Service.
Josef Urban has researched Automated theorem proving in several fields, including Selection, Artificial intelligence, Simple and Premise.
His most cited work include:
- A formal proof of the Kepler conjecture (160 citations)
- Learning-Assisted Automated Reasoning with Flyspeck (119 citations)
- MPTP 0.2: Design, Implementation, and Initial Experiments (119 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Mizar system, Mathematical proof, Programming language, Automated theorem proving and Theoretical computer science.
His Mizar system research is multidisciplinary, incorporating perspectives in Context, Formal mathematics, Division and Set.
His Mathematical proof research includes themes of HOL and Inference, Automated reasoning, Artificial intelligence.
His biological study spans a wide range of topics, including Structure, Proof assistant and XML.
Josef Urban interconnects Machine learning, Reinforcement learning, Axiom and Calculus in the investigation of issues within Automated theorem proving.
His Theoretical computer science study combines topics from a wide range of disciplines, such as Gas meter prover, Large set and Conjecture.
He most often published in these fields:
- Mizar system (35.79%)
- Mathematical proof (36.84%)
- Programming language (33.16%)
What were the highlights of his more recent work (between 2019-2021)?
- Programming language (33.16%)
- Gas meter prover (9.47%)
- Mathematical proof (36.84%)
In recent papers he was focusing on the following fields of study:
Josef Urban mainly focuses on Programming language, Gas meter prover, Mathematical proof, Proof assistant and Theoretical computer science.
His research integrates issues of Mizar system and Transformer in his study of Programming language.
His Gas meter prover research focuses on Automated theorem proving and how it connects with Rewriting, Algebra and Abelian group.
His study looks at the relationship between Mathematical proof and fields such as Reinforcement learning, as well as how they intersect with chemical problems.
His Proof assistant research is multidisciplinary, incorporating perspectives in Online machine learning and Scripting language.
His Theoretical computer science research integrates issues from Simple, Inference and Arity.
Between 2019 and 2021, his most popular works were:
- Exploration of neural machine translation in autoformalization of mathematics in Mizar (13 citations)
- First Neural Conjecturing Datasets and Experiments (12 citations)
- TacticToe: Learning to Prove with Tactics (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
Josef Urban spends much of his time researching Gas meter prover, Artificial intelligence, Artificial neural network, Mizar system and Theoretical computer science.
His Gas meter prover research is multidisciplinary, relying on both Automated theorem proving, Symbol, Embedding, Inference and Arity.
His Artificial intelligence research focuses on subjects like State, which are linked to Selection.
His Mizar system research includes themes of Context, Programming language, Transformer and Natural language processing.
His studies in Theoretical computer science integrate themes in fields like Monte Carlo tree search and Proof assistant, Mathematical proof.
His work deals with themes such as Rank, Language translation, Premise and Benchmark, which intersect with Recurrent neural network.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
