Dr Monika Seisenberger
Associate Professor
Computer Science
Telephone: (01792) 602131
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Specialist areas: Formal methods: Program extraction, interactive theorem proving, specification and verification. Logic: proof theory, infinitary combinatorics, in particular well- and better quasiorderings.

Areas of Expertise

  • Formal Methods
  • Program extraction
  • Interactive theorem proving
  • Specification and Verification
  • Logic
  • Proof theory
  • Well- and better quasiorderings

Publications

  1. & Higman’s Lemma and Its Computational Content. In Advances in Proof Theory. (pp. 353-375).
  2. & Towards Safety Analysis of ERTMS/ETCS Level 2 in Real-Time Maude. In Formal Techniques for Safety-Critical Systems. Springer.
  3. & Program extraction applied to monadic parsing. Journal of Logic and Computation, exv078
  4. & Extracting verified decision procedures: DPLL and Resolution. Logical Methods in Computer Science 11(1)
  5. & Verification of Solid State Interlocking Programs. In Counsell, Steve and Nunez, Manuel (Ed.), Software Engineering and Formal Methods. (pp. 253-268). Springer.

See more...

Teaching

  • CS-081 Computational Problem Solving

    This module is a continuation of the module CSC061: Introduction to Programming. In it, students will continue to enhance their skills in programming, as well as gain a basic understanding of algorithms and data structures.

  • 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.

  • CSC410 MSci Computer Science Project Research Methods

    This module will introduce students to some fundamental research methodologies and good practice in research. They will undertake background research including a literature review and specify the aims of their MSci final year project, and produce a plan for their proposed research.

  • CSCM70 Mathematical Skills for Data Scientists

    This course is an introductory course to the mathematical methods needed by a data scientist. It covers the basics of algebra, optimisation techniques, statistics, and Fourier analysis. The main goal of the class is for students to gain practical experience of the mathematical methods and tools that are essential in data science and that will be used in the other modules of this programme. The module is aimed at students with basic experience in mathematics.

  • CSP420 MSci Computer Science Project

    This module is the research based Level 7 project for MSci Computer Science students. It consists of a substantial written dissertation which usually involves both a research component and the implementation of a tool/software system.

Supervision

  • Centrality Metrics Detection of Terrorist Networks. (current)

    Student name:
    MSc
    Other supervisor: Dr Daniel Archambault
  • Proof-theoretic Methods in Natural Language Processing (current)

    Student name:
    PhD
    Other supervisor: Dr Ulrich Berger
    Other supervisor: Dr Anton Setzer
  • From Natural Language Proofs to Correct Programs. (current)

    Student name:
    PhD
    Other supervisor: Dr Ulrich Berger