Dr Michael Tautschnig
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Lecturer
Email: michael.tautschnig@qmul.ac.ukTelephone: +44 20 7882 5226Room Number: Peter Landin, CS 432Website: http://www.tautschnig.netOffice Hours: Tuesday 13:00-14:30
Teaching
Programming for Artificial Intelligence and Data Science (Postgraduate)
This module provides an intensive practical introduction to programming in Python, suitable for students with some degree of mathematical or statistical maturity. It covers a range of practical skills and underlying knowledge. These include the basic programming constructs for control, data structuring and modularisation; the use of systems for collaborative development and version control such as Git; unit testing and documentation; project structures and continuous integration/deployment.
Research
Research Interests:
Software VerificationConcurrency
Decision Procedures
Publications
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Delmas R, Hassan Z, Hu Q et al. (2026). Kani: A Model Checker for Rust. Proceedings of the 41st IEEE/ACM International Conference on Automated Software Engineering
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Peled R, Kroening D, Tautschnig M et al. (2026). Large Lemma Miners: Can LLMs Do Induction Proofs for Hardware?. Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence
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An C, Bayless S, Buliani S et al. (2026). A Neurosymbolic Approach to Natural Language Formalization and Verification. nameOfConference
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An C, Bayless S, Buliani S et al. (2026). A Neurosymbolic Approach to Natural Language Formalization and Verification. nameOfConference
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Delmas R, Hassan Z, Hu Q et al. (2026). Kani: A Model Checker for Rust. nameOfConference
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Peled R, Kroening D, Tautschnig M et al. (2026). Large Lemma Miners: Can LLMs do Induction Proofs for Hardware?. nameOfConference
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Cook B, Delmas R, Hassan Z et al. (2026). Verifying the Rust Standard Library. nameOfConference
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Cook B, Delmas R, Hassan Z et al. (2026). Verifying the Rust Standard Library. nameOfConference
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Bayer J, Zetzsche S, Bouissou O et al. (2026). Teaching LLMs Program Semantics via Symbolic Execution Traces. nameOfConference
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Giacobbe M, Kroening D, Pal A et al. (2025). Let a Neural Network be Your Invariant. Advances in Neural Information Processing Systems 38
DOI: 10.52202/085713-2509