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Publications

 

 Some of our Recent Refereed Papers

  • Contreras R., Smola F., Zheng J., Hillston J., Fleuriot J. D. (2024). Verifying properties of Activities of Daily Living. To appear in the Proceedings of DataMod  2024.
  • Georgiev K., Doudesis D., McPeake J., Mills N. L., Fleuriot J., Shenkin S. D. and Anand A (2024). Understanding hospital rehabilitation using electronic health records in patients with and without COVID-19. BMC Health Services Research (2024) 24:1245.  https://doi.org/10.1186/s12913-024-11665-x.
  • Blayney M. C., Reed M. J., Masterson J. A.,…, Fleuriot, et al. (2024) Multimorbidity and adverse outcomes following emergency department attendance: population based cohort study. BMJ Medicine 2024;3:e000731. doi: 10.1136/bmjmed-2023-000731
  • DeLong L. N. and Fleuriot J. D. (2024). Neurosymbolic AI on Knowledge Graphs: Characterization, Payoff, and Pitfalls. International Conference on Neural-Symbolic Learning and Reasoning (Recently published paper presentation, NeSy 2024).
  • DeLong L. N., Fernández Mir R. and Fleuriot J. D. (2024).Neurosymbolic AI for Reasoning Over Knowledge Graphs: A Survey. IEEE Transactions on Neural Networks and Learning Systems, 10.1109/TNNLS.2024.3420218.
  • Smola F. and Fleuriot J. D. (2024). Linear Resources in Isabelle/HOL. Journal of Automated Reasoning,  68(9), https://doi.org/10.1007/s10817-024-09698-2.
  • Ho L., Pugh C., Seth S., Arakelyan S., Lone N., Lyall M. J., Anand A., Fleuriot J. D., Galdi P., Guthrie B. (2024). Predicting short- to medium-term care home admission risk in older adults: a systematic review of externally validated models.  Age and Ageing, Volume 53, Issue 5, https://doi.org/10.1093/ageing/afae088.
  • Moreno G. R., Restocchi R., Fleuriot J. D., Anand A., Mercer S., Guthrie B. (2024). Multimorbidity analysis with low condition counts: A robust Bayesian approach for small but important subgroups.  eBioMedicine, Volume 102, https://doi.org/10.1016/j.ebiom.2024.105081.
  • Burton J. K.,  McMinn M., Vaughan J. E.,  Nightingale G., Fleuriot J., Guthrie B. (2024). Analysis of the impact of COVID-19 on Scotland’s care-homes from March 2020 to October 2021: national linked data cohort analysis. Age and Ageing, Volume 53, Issue 2,  https://doi.org/10.1093/ageing/afae015.
  • Ho L., Pugh C., Seth S., Arakelyan S., Lone N., Lyall M. J., Anand A., Fleuriot J. D., Galdi P., Guthrie B. (2024). Performance of models for predicting one to three year mortality in older adults: a systematic review of externally validated models. Lancet Healthy Longevity.
  • Zheng J., Papapanagiotou P., Fleuriot J. D. (2024). Alignment-based conformance checking over probabilistic events. Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS-57).
  • DeLong L. N., Gadiya Y., Fleuriot J. D., Domingo-Fernandez D. (2023). Neurosymbolic AI Reveals Biases and Limitations in ML-Driven Drug Discovery.  In New Frontiers of AI for Drug Discovery and Development, NeurIPS.
  • DeLong L. N., Fernández Mir R, Ji Z., Smith F. N. C., Fleuriot J. D. (2023). Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs. Proceedings of Knowledge and Logical Reasoning in the Era of Data-driven Learning, ICML. See also arXiv:2307.0841.
  • Livingstone S., Morales D. R., Fleuriot J., Donnan P. T., Guthrie B. (2023). External validation of the QLifetime cardiovascular risk prediction tool: population cohort study. BMC Cardiovasc Disord 23, 194. 
  • Charlton E. C., Poon M. T. C., Brennan P. and Fleuriot J. D. (2023). Development of prediction models for one-year brain tumour survival using machine learning: a comparison of accuracy and interpretability. Computer Methods and Programs in Biomedicine, Volume 233. https://doi.org/10.1016/j.cmpb.2023.107482.
  • Chevallier M., Whyte M. and Fleuriot J. D. (2022). Constrained Training of Neural Networks via Theorem Proving.  Proceedings of OVERLAY 2022,  as part of the 21st International Conference of the Italian Association for Artificial Intelligence (AIxIA 2022), CEUR, Volume 3311, pp. 7-12. Longer version available as arXiv:2207.03880.
  • Romero Moreno G., Restocchi V. and Fleuriot J. D. (2022). Building Co-morbidity Networks via Bayesian Network Reconstruction. Proceedings of the 11th International Conference on Complex Networks and their Applications.
  • Schmoetten R., Palmer J. E. and Fleuriot J. D. (2022). Towards Formalising Schutz’ Axioms for Minkowski Spacetime in Isabelle/HOL. Journal of Automated Reasoning. https://doi.org/10.1007/s10817-022-09643-1.
  • MacKenzie C., Huch F., Vaughan J. and Fleuriot J. D. (2022).  Re-imagining the Isabelle Archive of Formal Proofs. Intelligent Computer Mathematics. CICM 2022. Lecture Notes in Computer Science. Volume 13467.
  • Gunatilleke J., Fleuriot J. and Anand. A. (2022). A Literature Review on the Analysis of Symptom-based Clinical Pathways: Time for a Different Approach? PLOS Digital Health. https://doi.org/10.1371/journal.pdig.0000042.
  • Restocchi V., Gaete Villegas J. and Fleuriot J. D. (2022). Multimorbidity profiles and stochastic block modeling improve ICU patient clustering. Artificial Intelligence for Health 2022. AI4Health, Proceedings of IEEE/ACM CCGRID 2022, 925-932. https://doi.org/10.1109/CCGrid54584.2022.00112.
  • Schmoetten R., Palmer J.,  and Fleuriot J. D. (2021). Formalising Geometric Axioms for Minkowski Spacetime and Without-Loss-of-Generality Theorems. Proceedings of the 13th International Conference in Automated Deduction in Geometry, Electronic Proceedings in Theoretical Compuster Science (EPTCS) 352, 116-128.
  • Burton J, McMinn M., Vaughan J., Fleuriot J., and Guthrie B. (2021). Care-home outbreaks of COVID-19 in Scotland March to May 2020: national linked data cohort analysis.  Age and Ageing Journal, Volume 50, Issue 5, September 2021, 1482–1492, Oxford University Press.
  • Papapanagiotou P. and Fleuriot J. (2021). Object-level Reasoning with Logics Encoded in HOL Light. Electronic Proceedings in Theoretical Computer Science 332, pp. 18–34.
  • Fleuriot J. D. (2021). Mechanizing Mathematics: From Dream to Reality. Chapter to appear in Mathematical Reasoning: The History and Impact of the DReaM Group (Ed. G. Michaelson), Springer
  • Papapanagiotou P., Vaughan J., Smola F, and Fleuriot J. (2020). A Real-world Case Study of Process and Data Driven Predictive Analytics for Manufacturing Workflows. Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS-54).
  • Wingfield L., Ceresa C., Thorogood S., Fleuriot J, Knight S. (2020). Artificial Intelligence and Liver Transplant: Predicting Survival of Individual Grafts, A Systematic Review. In American Association for the Study of Liver Diseases,  Liver Transplantation,  Wiley. 
  • Wingfield L., Ceresa C., Fleuriot J, Knight S. (2019). Artificial Intelligence for Liver Transplant (AI4T): Predicting Graft Survival. ASiT/TMS Poster of Distinction Prize. Association of Surgeons in Training International Surgical Conference 2019, British Journal of Surgery (BJS), Volume 106, Issue S6. 
  • Papapanagiotou P. and Fleuriot J. (2019). A Pragmatic, Scalable Approach to Correct-by-construction Process Composition Using Classical Linear Logic Inference.  Logic-Based Program Synthesis and Transformation (Post-proceedings of LOPSTR 2018), LNCS Springer, Volume 11408, 77-93.
  • Fleuriot J., Wang D. and Calmet J. (Eds. 2018). Artificial Intelligence and Symbolic Computation. Lecture Notes in Artificial Intelligence, Volume 1110, Springer.
  • Palmer J. and Fleuriot J. (2018) Mechanising an Independent Axiom System for Minkowski Space-time. Proceedings of the 12th International Conference on Automated Deduction in Geometry, 64-79.
  • Morris I. and Fleuriot J. (2018). Towards a Mechanisation in Isabelle of Birkhoff’s Ruler and Protractor Geometry. Proceedings of the 12th International Conference on Automated Deduction in Geometry, 46-63.
  • Narboux J., Janicic P. and Fleuriot J. (2018). Computer-assisted Theorem Proving in Synthetic Geometry. Chapter in the Handbook of Geometric Constraint Systems Principles (ISBN 9781498738910), 21-44, Chapman and Hall/CRC, July 2018.
  • Jiang Y., Papapanagiotou P. and Fleuriot J. (2018). Machine Learning for Automated Inductive Theorem Proving. Proceedings of the 13th International Artificial Intelligence and Symbolic Computation (AISC) Conference 2018, Lecture Notes in Artificial Intelligence, Volume 11110, 87-103.
  • Papapanagiotou P. and Fleuriot J. (2018). Correct-by-construction Process Composition Using Classical Linear Logic Inference. Proceedings of Logic-Based Program Synthesis and Transformation (LOPSTR) Symposium 2018.
  • Wingfield L., Kulendran M., Khan O., Fleuriot J. (2017) Bringing Artificial Intelligence to Patient Care in Bariatric Surgery: A Feasibility Study. International Journal of Surgery, Volume 47 , S92.
  • Papapanagiotou P. and Fleuriot J. (2017). WorkflowFM: A Logic-based Formal Verification Framework for Process Specification and Composition. Proceedings of the 26th International Conference on Automated Deduction (CADE 26). LNCS Volume 10395, 357-370, Springer
  • Dewanti, A., Papapanagiotou, P., Gilhooly, C., Fleuriot, J., Manataki, A. & Moss, L. (2017). Development of workflow-based guidelines for the care of burns in Scotland. Proceedings of the 9th International Conference e-Health 2017,  155-158.
  • Alexandru C-A., Clutterbuck D., Papapanagiotou P., Fleuriot J. and Manataki A. (2017). A Step Towards the Standardisation of HIV Care Practices, 10th International Conference on Health Informatics.

Letter

Formal Proof Libraries

Working Papers/Pre-prints

  • DeLong L. N., Gadiya Y., Galdi P., Fleuriot J. D., Domingo-Fernándes D. MARS: A neurosymbolic approach for interpretable drug discovery. arXiv:2410.05289.
  • Schmoetten R. and Fleuriot J. D.  (2024). A Construction of the Lie Algebra of a Lie Group in Isabelle/HOL. arXiv:2407.19211.
  • DeLong L. N., Fleetwood K., Prigge R., Galdi P., Guthrie B., Fleuriot J. D. (2024). Investigating associations between physical multimorbidity clusters and subsequent depression: cluster and survival analysis of UK Biobank data. medRXiv.
  • Romero Moreno G., Restocchi V. and Fleuriot J. D., Atul A. and Mercer S.  and Guthrie B. (2023) Associations between Morbidities in Small But Important Subgroups: A Novel Bayesian Approach for Robust Multimorbidity Analysis with Small Sample Sizes. Available at SSRN: https://ssrn.com/abstract=4515875 or http://dx.doi.org/10.2139/ssrn.4515875. Updated and published in eBioMedicine, Volume 102, https://doi.org/10.1016/j.ebiom.2024.105081.
  • Georgiev K., Doudesis D., McPeake J., Mills N. L., Fleuriot J., Shenkin S. D., Anand A. (2023). Understanding quantity and intensity of hospital rehabilitation using electronic health record data. medRxiv 2023.05.25.23290461; doi: https://doi.org/10.1101/2023.05.25.23290461. Updated and published in BMC Health Services Research (2024) 24:1245.
  • DeLong L. N., Fernández Mir R., Whyte M., Ji Z., Fleuriot J. D. (2023). Neurosymbolic AI for Reasoning on Graph Structures: A Survey. arXiv:2302.07200. Updated and publised in IEEE Transactions on Neural Networks and Learning Systems.
  • Charlton, C. E.  and Poon, M. T. C.  and Brennan, P and Fleuriot, J. D. (2022). Comparing the Interpretability of Machine Learning Classifiers for Brain Tumour Survival Prediction. Available at SSRN: https://ssrn.com/abstract=4164349 or http://dx.doi.org/10.2139/ssrn.4164349. Final version published at https://doi.org/10.1016/j.cmpb.2023.107482. Updated and published in Computer Methods and Programs in Biomedicine, Volume 233.
  • Chevallier M., Whyte M., and Fleuriot J. (2022). Constrained Training of Neural Networks via Theorem Proving. arXiv:2207.03880.
  • Chevallier M. and Fleuriot J.  (2021). Formalising the Foundations of Discrete Reinforcement Learning in Isabelle/HOL. arXiv:2112.05996.
  • Schmoetten R., Palmer J. E., Fleuriot J. D. (2021). Towards Formalising Schutz’ Axioms for Minkowski Spacetime in Isabelle/HOL. arXiv:2108.10868. Updated and published in the Journal of Automated Reasoning, https://doi.org/10.1007/s10817-022-09643-1.
  • Charlton C. E., Poon M. T. C., Brennan P. M. and Fleuriot J. D. (2021). Interpretable Machine Learning Classifiers for Brain Tumour Survival Prediction. arXiv:2106.0942.
  • MacKenzie C., Fleuriot J. and Vaughan J. (2021). An Evaluation of the Archive of Formal Proofs. arXiv:2104.01052.
  • Scott P. and Fleuriot J. D. (2019). Where are the Natural Numbers in Hilbert’s Foundations of Geometry? arXiv:1911.07057.

Recent PhD Theses

  • Jiawei Zheng (2024).
  • Jorge Gaete Villegas (2024).
  • Mark Chevallier (2023).
  • Imogen Morris (2022).
  • Jake Palmer (2022). A rigorous treatment of Meek’s method for Single Transferable Vote with formal proofs of key properties, School of Informatics, University of Edinburgh.
  • Yaqing Jiang (2019). Machine learning for inductive theorem proving, School of Informatics, University of Edinburgh.

Recent Masters Theses

  • Matthey Whyte (2023). Formalising Tensors in Isabelle/HOL – A Neurosymbolic Pipeline from Formal Specifications to Efficient Machine Learning Code. MSc in Artificial Intelligence, School of Informatics, University of Edinburgh. One of the outstanding MSc theses of the academic year 2022-23.
  • Scott O’Donoghue (2022). Applying Machine Learning and Interpretation Techniques to Persistent Critical Illness. Msc in Data Science, Technology, and Innovation, School of Informatics, University of Edinburgh.  Awarded with Distinction.
  • Callum Abbott (2021). To Drain or Not to Drain? A Causal Investigation into the Efficacy of Subdural Drains in Preventing CSDH Recurrence, School of Mathematics, University of Edinburgh. Winner of the MSc in Data Science thesis prize. Supervisors: J. D. Fleuriot, in collaboration with Paul Brennan and Michael Poon.
  • Mathis Gerdes (2021). A Mechanized Investigation of an Axiomatic System for Minkowski Spacetime. MSc in Artificial Intelligence, School of Informatics, University of Edinburgh. One of the outstanding MSc theses of the academic year 2019-20. Supervisors: J. D. Fleuriot, R. Schmoetten and J. Palmer.
  • Richard Schmoetten (2020). Axiomatic Minkowski Spacetime in Isabelle/HOL. MSc in Informatics, School of Informatics, University of Edinburgh. One of the outstanding MSc theses of the academic year 2019-20 and Winner of the  MSc in Informatics thesis prize. Supervisors: J. D. Fleuriot and J. Palmer
  • Colleen Charlton (2020). Building an Interpretable MachineLearning Classifier for thePrediction of Brain TumourSurvival. MSc in Cognitive Science, School of Informatics, University of Edinburgh. One of the outstanding MSc theses of the academic year 2019-20. Supervisor: J. D. Fleuriot, in collaboration with Paul Brennan and Michael Poon.
  • Anita Klementiev (2020). Evaluation of Process Mining Techniques for Modeling In-Hospital Patient Care Pathways Using the MIMIC-III Dataset. MSc in Cognitive Science, School of Informatics, University of Edinburgh. MSc thesis awarded with Distinction. Supervisors: J. D. Fleuriot and C. Stables.
  • Simon Thorogood (2019). Predicting Transplant and Patient Survival Following Liver Transplantation using Machine Learning. MSc in Data Science, School of Informatics, University of Edinburgh. One of the outstanding MSc theses of academic year 2018-19 and Winner of the Informatics MSc in Data Science thesis prize. Supervisor: J. D. Fleuriot, in collaboration with L. Wingfield and S. Knight.
  • Callum Biggs O’May (2019). Investigating Brain Cancer Survival with Machine Learning. MSc in Artificial Intelligence, School of Informatics, University of Edinburgh. One of the outstanding MSc theses of academic year 2018-19. Supervisor: J. D. Fleuriot, in collaboration with Paul Brennan.
  • Jessika Rockel (2019). Exploring Euler’s Foundations of Differential Calculus in Isabelle/HOL using Nonstandard Analysis. MSc in Computer Science, School of Informatics, University of Edinburgh. One of the outstanding MSc theses of academic year 2018-19. Supervisor: J. D. Fleuriot.
  • Ka Wing Pang (2019). Exploring streams with Isabelle/HOL. MSc in Computer Science, School of Informatics, University of Edinburgh. MSc thesis awarded with Distinction. Supervisor: J. D. Fleuriot.