About
Dr Cécilia Pradic is a member of staff in the Department of Computer Science at Swansea University. Personal webpage: https://cpradic.web.deuxfleurs.fr
Dr Cécilia Pradic
Lecturer in Computer Science
Computer Science
Office - 410
Fourth Floor
Computational Foundry
Bay Campus
Fourth Floor
Computational Foundry
Bay Campus
Research Links
Languages Spoken
- French
Publications
Research Highlights
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Franklin, J., Neumann, E., Pauly, A., Pradic, C., & Valenti, M. (2025). Represented Spaces of Represented Spaces. In Lecture Notes in Computer Science (pp. 47-61). Springer Nature Switzerland.
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Neumann, E., Pauly, A., Pradic, C., & Valenti, M. (2025). Computably Discrete Represented Spaces. In Lecture Notes in Computer Science (pp. 349-364). Springer Nature Switzerland.
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Pradic, C. & Price, I. (2025). Weihrauch Problems as Containers. In Arnold Beckmann, Isabel Oitavem, Florin Manea (Ed.), Lecture Notes in Computer Science (pp. 395-409). Springer Nature Switzerland.
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Benedikt, M., Pradic, C., & Wernhard, C. (2024). Synthesizing nested relational queries from implicit specifications: via model theory and via proof theory. Logical Methods in Computer Science, 20(3)
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Pradic, C. & Price, I. (2024). Implicit Automata in λ-calculi III: Affine Planar String-to-string Functions. Electronic Notes in Theoretical Informatics and Computer Science, Volume 4 - Proceedings of MFPS XL
All Research
Journal Articles
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Pradic, C. & Price, I. (2024). Implicit Automata in λ-calculi III: Affine Planar String-to-string Functions. Electronic Notes in Theoretical Informatics and Computer Science, Volume 4 - Proceedings of MFPS XL
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Pauly, A., Pradic, C., & Solda, G. (2024). On the Weihrauch degree of the additive Ramsey theorem. Computability, 13(3-4), 459-483.
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Benedikt, M., Pradic, C., & Wernhard, C. (2024). Synthesizing nested relational queries from implicit specifications: via model theory and via proof theory. Logical Methods in Computer Science, 20(3)
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Benedikt, M. & Pradic, C. (2021). Generating collection transformations from proofs. Proceedings of the ACM on Programming Languages, 5(POPL), 1-28.
Conference Contributions
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Franklin, J., Neumann, E., Pauly, A., Pradic, C., & Valenti, M. (2025). Represented Spaces of Represented Spaces. In Lecture Notes in Computer Science (pp. 47-61). Springer Nature Switzerland.
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Neumann, E., Pauly, A., Pradic, C., & Valenti, M. (2025). Computably Discrete Represented Spaces. In Lecture Notes in Computer Science (pp. 349-364). Springer Nature Switzerland.
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Pradic, C. & Price, I. (2025). Weihrauch Problems as Containers. In Arnold Beckmann, Isabel Oitavem, Florin Manea (Ed.), Lecture Notes in Computer Science (pp. 395-409). Springer Nature Switzerland.
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Benedikt, M., Pradic, C., & Wernhard, C. (2023). Synthesizing Nested Relational Queries from Implicit Specifications. In Proceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database SystemsACM.
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Nguyễn, L., Noûs, C., & Pradic, C. (2021). Comparison-Free Polyregular Functions.. In Nikhil Bansal, Emanuela Merelli, James Worrell (Ed.), 48th International Colloquium on Automata, Languages, and Programming (ICALP 2021) (pp. 139:1-139:20). Schloss Dagstuhl -- Leibniz-Zentrum für Informatik.
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Nguyên, L. & Pradic, C. (2020). Implicit Automata in Typed λ-Calculi I: Aperiodicity in a Non-Commutative Logic. In 47th International Colloquium on Automata, Languages and Programming (ICALP 2020) (pp. 135:1-135:20). Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik.
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Pradic, C. & Riba, C. (2019). A Dialectica-Like Interpretation of a Linear MSO on Infinite Words. In Lecture Notes in Computer Science (pp. 470-487). Springer International Publishing.
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Doumane, A., Kuperberg, D., Pous, D., & Pradic, C. (2019). Kleene Algebra with Hypotheses. In Lecture Notes in Computer Science (pp. 207-223). Springer International Publishing.
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Pradic, C. & Riba, C. (2018). LMSO: A Curry-Howard Approach to Church's Synthesis via Linear Logic. In Proceedings of the 33rd Annual ACM/IEEE Symposium on Logic in Computer ScienceACM.
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Kolodziejczyk, L., Michalewski, H., Pradic, C., & Skrzypczak, M. (2016). The logical strength of Büchi's decidability theorem. In 25th EACSL Annual Conference on Computer Science Logic (CSL 2016) (pp. 36:1-36:16). Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik.
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Krishnaswami, N., Pradic, C., & Benton, N. (2015). Integrating Linear and Dependent Types. In POPL '15: Proceedings of the 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages (pp. 17-30). Association for Computing Machinery (ACM).
Supervision
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Comparing the Expressiveness of Transducers and Simply-Typed Linear λ-Calculi (current)
PhDOther supervisor: Dr Arno Pauly -
MECHANICAL FORMALISATION OF NESTED RELATIONAL CALCULUS QUERY SYNTAX AND SEMANTICS (awarded 2024)
MRes
Taught Modules
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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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CSCM12 Software Concepts and Efficiency
This module provides a solid introduction to algorithm design, complexity analysis and data structure for efficient algorithm development. The module will involve students developing and evaluating their own algorithms. Java will be used as the main programming language.
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CSCM712 Problem Solving & Software Efficiency
This module provides a comprehensive introduction to Python programming, covering essential concepts and advanced techniques. Students will also learn about complexity/efficiency measures. The course will delve into tracing execution, defining algorithms and specifications, and understanding algorithmic problems and running time. By the end of the course, students will have a solid foundation in Python programming and the ability to appreciate the complexity of computational problems.
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CSCM712Q Problem Solving & Software Efficiency
This module provides a comprehensive introduction to Python programming, covering essential concepts and advanced techniques. Students will also learn about complexity/efficiency measures. The course will delve into tracing execution, defining algorithms and specifications, and understanding algorithmic problems and running time. By the end of the course, students will have a solid foundation in Python programming and the ability to appreciate the complexity of computational problems.