Trosolwg
Mae Monika Seisenberger yn Athro Cysylltiol yn yr Adran Gyfrifiadureg ym Mhrifysgol Abertawe.
Dr Monika Seisenberger
Athro Cyswllt
Computer Science
405
Pedwerydd Llawr
Y Ffowndri Gyfrifiadol
Campws y Bae
Pedwerydd Llawr
Y Ffowndri Gyfrifiadol
Campws y Bae
Ar gael ar gyfer Goruchwyliaeth Ôl-raddedig
Dolenni Ymchwil
Meysydd Arbenigedd
- Dulliau Ffurfiol
- Coladu a thynnu gwybodaeth o raglenni
- Profi theorem ryngweithiol
- Manylu a Gwirio
Cyhoeddiadau
Uchafbwyntiau Ymchwil
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Duell, J., Seisenberger, M., Fu, H., & Fan, X. (2025). QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations. In 2024 IEEE International Conference on Data Mining (ICDM) (pp. 693-698). IEEE.
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Morgan, J., Paiement, A., Seisenberger, M., Williams, J., & Wyner, A. (2018). A Chatbot Framework for the Children’s Legal Centre. In Frontiers in Artificial Intelligence and Applications (pp. 205-209).
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Moghaddas, R., Caraffini, F., & Seisenberger, M. (2025). A Multi-Agent System for Optimal Train Scheduling in Single-Track Railways. In Applications of Evolutionary Computation: 28th European Conference, EvoApplications 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, Proceedings, Part II (pp. 320-333). Springer.
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Duell, J., Seisenberger, M., Zhong, T., Fu, H., & Fan, X. (2023). A Formal Introduction to Batch-Integrated Gradients for Temporal Explanations. In 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI)IEEE.
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Duell, J., Seisenberger, M., & Fan, X. (2023). Counterfactual-Integrated Gradients: Counterfactual Feature Attribution for Medical Records. In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)IEEE.
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Banerjee, M., Cai, V., Lakshmanappa, S., Lawrence, A., Roggenbach, M., Seisenberger, M., & Werner, T. (2023). A Tool-Chain for the Verification of Geographic Scheme Data. In Editors: Birgit Milius, Simon Collart-Dutilleul, Thierry Lecomte (Ed.), Lecture Notes in Computer Science (pp. 211-224). Springer Nature Switzerland.
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Seisenberger, M., Beek, M., Fan, X., Ferrari, A., Haxthausen, A., James, P., Lawrence, A., Luttik, B., Pol, J., & Wimmer, S. (2022). Safe and Secure Future AI-Driven Railway Technologies: Challenges for Formal Methods in Railway. In Lecture Notes in Computer Science (pp. 246-268). Springer Nature Switzerland.
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Duell, J., Fan, X., & Seisenberger, M. (2022). Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers. In Lecture Notes in Computer Science (pp. 411-420). Springer International Publishing.
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Kovvuri, V., Liu, S., Seisenberger, M., Fan, X., Muller, B., & Fu, H. (2022). On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study. In 2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1-8). IEEE.
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Berger, U., Seisenberger, M., Spreen, D., & Tsuiki, H. (2023). Concurrent Gaussian Elimination. In Marco Benini, Olaf Beyersdorff, Michael Rathjen, Peter Schuster (Ed.), Mathematics for Computation (M4C) (pp. 223-250). WORLD SCIENTIFIC
Holl Ymchwil
Erthyglau Cylchgronau
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Berger, U., James, P., Lawrence, A., Roggenbach, M., & Seisenberger, M. (2018). Verification of the European Rail Traffic Management System in Real-Time Maude. Science of Computer Programming, 154, 61-88.
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Berger, U., Jones, A., & Seisenberger, M. (2015). Program extraction applied to monadic parsing. Journal of Logic and Computation, exv078
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Berger, U., Lawrence, A., Forsberg, F., & Seisenberger, M. (2015). Extracting verified decision procedures: DPLL and Resolution. Logical Methods in Computer Science, 11(1)
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Berger, U. & Seisenberger, M. (2012). Proofs, Programs, Processes. Theory of Computing Systems, 51(3), 313-329.
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Lawrence, A., Berger, U., & Seisenberger, M. (2012). Extracting a DPLL Algorithm. Electronic Notes in Theoretical Computer Science, 286, 243-256.
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Seisenberger, M. (2008). Programs from proofs using classical dependent choice. Annals of Pure and Applied Logic, 153(1-3), 97-110.
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Berger, U. & Seisenberger, M. (2005). Applications of inductive definitions and choice principles to program synthesis. Oxford Logic Guides, 48, 137-148.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa16458
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Seisenberger, M. (2001). Kruskal's tree theorem in a constructive theory of inductive definitions. Synthese Library, 306, 241-255.SU Repository: https://cronfa.swan.ac.uk/Record/cronfa16457
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Berger, U., Helmut, S., Monika, S., & Seisenberger, M. (2001). The Warshall Algorithm and Dickson's Lemma: Two examples of realistic program extraction. Journal of Automated Reasoning, 26(2), 205-221.
Llyfrau Awdurol
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Seisenberger, M., Schuster, P., & Weiermann, A. (2020). Well-Quasi Orders in Computation, Logic, Language and Reasoning Springer International Publishing.
Penodau Llyfrau
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Berger, U. & Seisenberger, M. (2023). On the Computational Content of Choice Principles. In Douglas Bridges Hajime Ishihara Michael Rathjen Helmut Schwichtenberg (Ed.), Handbook of Constructive Mathematics (pp. 806-825). Cambridge University Press
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Berger, U., Seisenberger, M., Spreen, D., & Tsuiki, H. (2023). Concurrent Gaussian Elimination. In Marco Benini, Olaf Beyersdorff, Michael Rathjen, Peter Schuster (Ed.), Mathematics for Computation (M4C) (pp. 223-250). WORLD SCIENTIFIC
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James, P., Lawrence, A., Roggenbach, M., & Seisenberger, M. (2016). Towards Safety Analysis of ERTMS/ETCS Level 2 in Real-Time Maude. Formal Techniques for Safety-Critical Systems Springer
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Schwichtenberg, H., Seisenberger, M., & Wiesnet, F. (2016). Higman’s Lemma and Its Computational Content. Advances in Proof Theory (pp. 353-375).
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James, P., Lawrence, A., Moller, F., Roggenbach, M., Seisenberger, M., Setzer, A., Kanso, K., & Chadwick, S. (2014). Verification of Solid State Interlocking Programs. Software Engineering and Formal Methods (pp. 253-268). Springer
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Ulrich, B. & Seisenberger, M. (2010). Program extraction via typed realisability for induction and coinduction. Ways of Proof Theory (pp. 157-181). Ontos VerlagSU Repository: https://cronfa.swan.ac.uk/Record/cronfa8074
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Benl, H., Berger, U., Schwichtenberg, H., Seisenberger, M., & Zuber, W. (1998). Proof theory at work: Program development in the Minlog system. Automated Deduction - A Basis for Applications (pp. 41-71). KluwerSU Repository: https://cronfa.swan.ac.uk/Record/cronfa16456
Cyfraniadau Cynadleddau
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Moghaddas, R., Caraffini, F., & Seisenberger, M. (2025). A Multi-Agent System for Optimal Train Scheduling in Single-Track Railways. In Applications of Evolutionary Computation: 28th European Conference, EvoApplications 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23–25, 2025, Proceedings, Part II (pp. 320-333). Springer.
-
Duell, J., Seisenberger, M., Fu, H., & Fan, X. (2025). QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations. In 2024 IEEE International Conference on Data Mining (ICDM) (pp. 693-698). IEEE.
-
Duell, J., Seisenberger, M., Zhong, T., Fu, H., & Fan, X. (2023). A Formal Introduction to Batch-Integrated Gradients for Temporal Explanations. In 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI)IEEE.
-
Duell, J., Seisenberger, M., & Fan, X. (2023). Counterfactual-Integrated Gradients: Counterfactual Feature Attribution for Medical Records. In 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)IEEE.
-
Banerjee, M., Cai, V., Lakshmanappa, S., Lawrence, A., Roggenbach, M., Seisenberger, M., & Werner, T. (2023). A Tool-Chain for the Verification of Geographic Scheme Data. In Editors: Birgit Milius, Simon Collart-Dutilleul, Thierry Lecomte (Ed.), Lecture Notes in Computer Science (pp. 211-224). Springer Nature Switzerland.
-
Seisenberger, M., Beek, M., Fan, X., Ferrari, A., Haxthausen, A., James, P., Lawrence, A., Luttik, B., Pol, J., & Wimmer, S. (2022). Safe and Secure Future AI-Driven Railway Technologies: Challenges for Formal Methods in Railway. In Lecture Notes in Computer Science (pp. 246-268). Springer Nature Switzerland.
-
Duell, J., Fan, X., & Seisenberger, M. (2022). Towards Polynomial Adaptive Local Explanations for Healthcare Classifiers. In Lecture Notes in Computer Science (pp. 411-420). Springer International Publishing.
-
Kovvuri, V., Liu, S., Seisenberger, M., Fan, X., Muller, B., & Fu, H. (2022). On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study. In 2022 International Conference on INnovations in Intelligent SysTems and Applications (INISTA) (pp. 1-8). IEEE.
-
Morgan, J., Paiement, A., Seisenberger, M., Williams, J., & Wyner, A. (2018). A Chatbot Framework for the Children’s Legal Centre. In Frontiers in Artificial Intelligence and Applications (pp. 205-209).
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Berger, U., Miyamoto, K., Schwichtenberg, H., & Seisenberger, M. (2011). Minlog - A Tool for Program Extraction Supporting Algebras and Coalgebras. In Lecture Notes In Computer Science (pp. 393-399).
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Berger, U. & Seisenberger, M. (2010). Proofs, Programs, Processes. In Lecture Notes In Computer Science (pp. 39-48).
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Seisenberger, M. (2001). An Inductive Version of Nash-Williams’ Minimal-Bad-Sequence Argument for Higman’s Lemma. In Lecture Notes In Computer Science (pp. 233-242). Springer.
Goruchwyliaeth
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Towards Ubiquitous Formal Software Verification for Highly Concurrent C and C++ (cyfredol)
PhDGoruchwyliwr arall: Prof Markus Roggenbach -
Creating Synthetic Test Data for Rail Design Tools (cyfredol)
PhDGoruchwyliwr arall: Prof Markus Roggenbach -
Advancing AI-Driven Techniques for Complex Problem Solving: A Focus on Distributed Constraint Satisfaction Problems (cyfredol)
PhDGoruchwyliwr arall: Dr Fabio Caraffini -
SMT Solving for Graph-Theoretic Representations of Railway Systems (cyfredol)
PhDGoruchwyliwr arall: Dr Anton Setzer -
Proof Checking for SMT-Solving and its application in the Railway Domain (cyfredol)
PhDGoruchwyliwr arall: Dr Anton Setzer -
Explainable Artificial Intelligence across Domains: Refinement of SHAP and Practical Applications (dyfarnwyd 2024)
PhDGoruchwyliwr arall: Dr Bertie Muller -
Explainable Artificial Intelligence for Medical Science (dyfarnwyd 2024)
PhDGoruchwyliwr arall: Prof Gert AartsGoruchwyliwr arall: Dr Xiuyi FanGoruchwyliwr arall: Dr Xiuyi Fan -
Testing of Railway RBCs in ERTMS Level 2 using Formal Methods (dyfarnwyd 2024)
PhDGoruchwyliwr arall: Prof Markus Roggenbach -
General Solving Engines Lifting Search Techniques to be used in General Purpose Programming Language (dyfarnwyd 2025)
PhDGoruchwyliwr arall: Dr Oliver Kullmann -
Show me how it's wrong: Counterexample Visualisation in Static Railway Verification (dyfarnwyd 2024)
MScGoruchwyliwr arall: Prof Markus Roggenbach
Modiwlau a Addysgir
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CS-205 Declarative Programming
This module provides an introduction to the functional and logic programming paradigms and gives students the opportunity to gain practical experience in using both.
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CSCM985 Logic, Modelling and Verification
This module provides a comprehensive introduction to logic and verification, emphasising the importance of logic in computer science. It covers propositional and predicate logic, including syntax, semantics, truth tables, logical equivalence, and natural deduction. Students will explore proof techniques, SAT and SMT solving, and transition from informal to formal specification. The module also delves into software verification, focusing on the verification of functional and imperative programs, and advanced topics such as domain and state-based modelling, and program extraction.