About
Monika Seisenberger is an Associate Professor in the Department of Computer Science, Swansea University.
Dr Monika Seisenberger
Associate Professor
Computer Science
Office - 405
Fourth Floor
Computational Foundry
Bay Campus
Fourth Floor
Computational Foundry
Bay Campus
Available For Postgraduate Supervision
Areas Of Expertise
- Formal Methods
- Program extraction
- Interactive theorem proving
- Specification and Verification
- Logic
- Proof theory
- Well- and better quasiorderings
Publications
Research Highlights
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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.
All Research
Journal Articles
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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.
Authored Books
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Seisenberger, M., Schuster, P., & Weiermann, A. (2020). Well-Quasi Orders in Computation, Logic, Language and Reasoning Springer International Publishing.
Book Chapters
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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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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., Rathjen, M., & Schuster, P. (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
Conference Contributions
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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.
-
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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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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Morgan, J., Morgan, J., Paiement, A., Williams, J., Wyner, A., & Seisenberger, M. (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.
Supervision
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Creating Synthetic Test Data for Rail Design Tools (current)
PhDOther supervisor: Prof Markus Roggenbach -
Advancing AI-Driven Techniques for Complex Problem Solving: A Focus on Distributed Constraint Satisfaction Problems (current)
PhDOther supervisor: Dr Fabio Caraffini -
SMT Solving for Graph-Theoretic Representations of Railway Systems (current)
PhDOther supervisor: Dr Anton Setzer -
Proof Checking for SMT-Solving and its application in the Railway Domain (current)
PhDOther supervisor: Dr Anton Setzer -
Explainable Artificial Intelligence across Domains: Refinement of SHAP and Practical Applications (awarded 2024)
PhDOther supervisor: Dr Bertie Muller -
Explainable Artificial Intelligence for Medical Science (awarded 2024)
PhDOther supervisor: Prof Gert AartsOther supervisor: Dr Xiuyi FanOther supervisor: Dr Xiuyi Fan -
Testing of Railway RBCs in ERTMS Level 2 using Formal Methods (current)
PhDOther supervisor: Prof Markus Roggenbach -
General Solving Engines«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /» Lifting Search Techniques to be used in General«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /»«br /» Purpose Programming Languages (current)
PhDOther supervisor: Dr Oliver Kullmann -
A SAT solver in Haskell & Agda (current)
MResOther supervisor: Dr Arno Pauly -
Show me how it's wrong: Counterexample Visualisation in Static Railway Verification (current)
MScOther supervisor: Prof Markus Roggenbach
Taught Modules
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CS-081 Computational Problem Solving
This module is a continuation of the module CSC061: Introduction to Programming. Students will continue to enhance their skills in programming, from a problem-solving point of view, and gain a basic understanding of algorithms and data structures.
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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.