What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Algorithm
- Programming language
Marta Kwiatkowska mainly investigates Probabilistic logic, Theoretical computer science, Model checking, Algorithm and Formal verification.
Her Probabilistic logic research includes elements of Automaton, Markov decision process, Nondeterministic algorithm and Temporal logic.
Her Theoretical computer science research incorporates elements of Range, Probabilistic analysis of algorithms, Statistical model and Markov model.
Her Model checking study combines topics in areas such as Theory of computation, Binary decision diagram, Markov chain and Probabilistic automaton.
Her research in Algorithm intersects with topics in Probability distribution and Mathematical optimization.
Her Formal verification study integrates concerns from other disciplines, such as Interference, Probabilistic relevance model, Fixed-point iteration and Bisimulation.
Her most cited work include:
- PRISM 4.0: verification of probabilistic real-time systems (1627 citations)
- PRISM : A tool for automatic verification of probabilistic systems (571 citations)
- PRISM: Probabilistic Symbolic Model Checker (557 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of study are Probabilistic logic, Theoretical computer science, Model checking, Algorithm and Markov decision process.
Her study looks at the intersection of Probabilistic logic and topics like Markov chain with Markov process.
Her Theoretical computer science research is multidisciplinary, incorporating elements of Statistical model and Component.
As part of the same scientific family, Marta Kwiatkowska usually focuses on Model checking, concentrating on Mathematical optimization and intersecting with Set.
Her biological study spans a wide range of topics, including Upper and lower bounds, State space and Robustness.
Her research investigates the connection between Robustness and topics such as Deep learning that intersect with problems in Artificial neural network.
She most often published in these fields:
- Probabilistic logic (39.95%)
- Theoretical computer science (30.50%)
- Model checking (22.46%)
What were the highlights of her more recent work (between 2016-2021)?
- Probabilistic logic (39.95%)
- Robustness (8.04%)
- Artificial intelligence (9.22%)
In recent papers she was focusing on the following fields of study:
Marta Kwiatkowska mainly focuses on Probabilistic logic, Robustness, Artificial intelligence, Algorithm and Mathematical optimization.
Her Probabilistic logic research integrates issues from Model checking, Automaton, Theoretical computer science, Temporal logic and Markov decision process.
Her work focuses on many connections between Model checking and other disciplines, such as Cryptographic protocol, that overlap with her field of interest in Scalability.
Her research integrates issues of Machine learning and Software in her study of Artificial intelligence.
Marta Kwiatkowska focuses mostly in the field of Algorithm, narrowing it down to topics relating to Upper and lower bounds and, in certain cases, Interval.
Marta Kwiatkowska has included themes like Reliability, Formal methods, Hybrid system, Markov chain and Discretization in her Mathematical optimization study.
Between 2016 and 2021, her most popular works were:
- Safety Verification of Deep Neural Networks (413 citations)
- Concolic testing for deep neural networks (144 citations)
- Feature-Guided Black-Box Safety Testing of Deep Neural Networks (115 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Operating system
Marta Kwiatkowska focuses on Robustness, Artificial intelligence, Mathematical optimization, Probabilistic logic and Deep neural networks.
Her Robustness study incorporates themes from Artificial neural network, Algorithm, Upper and lower bounds and Lipschitz continuity.
The concepts of her Algorithm study are interwoven with issues in Boolean combination and Software tool.
Her Mathematical optimization research is multidisciplinary, incorporating perspectives in Parametric statistics, Reliability, Class, Theory of computation and Markov chain.
The Probabilistic logic study combines topics in areas such as Wireless, MNIST database and Theoretical computer science, Temporal logic.
Her study looks at the relationship between Temporal logic and topics such as Markov decision process, which overlap with Model checking, Markov model, Hybrid system and Formal methods.
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