Mr Lachlan Simpson

Mr Lachlan Simpson

School of Electrical and Mechanical Engineering

College of Engineering and Information Technology

My research leverages tools from differential geometry and Lie theory to develop robust and user-friendly explainable AI models. I also work in developing physics-informed neural networks (PINNs) for problems in wave physics. In particular, I have applied PINNs to quantum graphs to engineer desirable properties of lattices. 

Date Institution name Country Title
2026 Adelaide University Australia PhD
2022 University of Adelaide Australia Honours Bachelor of Computer and Mathematical Sciences (Pure Mathematics)
2021 University of Adelaide Australia Bachelor of Computer and Mathematical Sciences

Year Citation
2026 Simpson, L., Costanza, F., Millar, K., Cheng, A., Lim, C. -C., & Gunn Chew, H. (2026). Algebraic adversarial attacks on explainability models. Machine Learning: Engineering, 2(1), 015001.
DOI

Year Citation
2026 Costanza, F., & Simpson, L. (2026). Riemannian Integrated Gradients: A Geometric View of Explainable AI. In Lecture Notes in Computer Science (Vol. 16033 LNCS, pp. 161-169). Springer Nature Switzerland.
DOI Scopus1
2026 Simpson, L., Millar, K., Cheng, A., Lim, C. C., & Chew, H. G. (2026). Graph-Based Integrated Gradients for Explaining Graph Neural Networks. In M. Liu, X. Yu, C. Xu, & Y. Song (Eds.), Lecture Notes in Computer Science (Vol. 16370 LNAI, pp. 150-162). SPRINGER-VERLAG SINGAPORE PTE LTD.
DOI Scopus1
2026 Simpson, L., Millar, K., Cheng, A., Lim, C. C., & Chew, H. G. (2026). Probabilistic Lipschitzness and the Stable Rank for Measuring XAI Model Robustness. In M. Liu, X. Yu, C. Xu, & Y. Song (Eds.), Lecture Notes in Computer Science (Vol. 16370 LNAI, pp. 137-149). SPRINGER-VERLAG SINGAPORE PTE LTD.
DOI

Year Citation
2024 Simpson, L., Costanza, F., Millar, K., Cheng, A., Lim, C. C., & Chew, H. G. (2024). Algebraic Adversarial Attacks on Integrated Gradients. In Proceedings - International Conference on Machine Learning and Cybernetics (pp. 26-31). Hybrid, Miyazaki: IEEE.
DOI Scopus2
2024 Simpson, L., Costanza, F., Millar, K., Cheng, A., Lim, C. C., & Chew, H. G. (2024). Tangentially Aligned Integrated Gradients for User-Friendly Explanations. In CEUR Workshop Proceedings Vol. 3910 (pp. 1-12). Dublin, Ireland: CEUR-WS.
Scopus1
2023 Simpson, L., Millar, K., Cheng, A., Chew, H. G., & Lim, C. C. (2023). A Testbed for Automating and Analysing Mobile Devices and Their Applications. In Proceedings - International Conference on Machine Learning and Cybernetics (pp. 201-208). Online: IEEE.
DOI Scopus1
2022 Millar, K., Simpson, L., Cheng, A., Chew, H. G., & Lim, C. (2022). Detecting Botnet Victims Through Graph-Based Machine Learning. In 2021 International Conference on Machine Learning and Cybernetics (ICMLC) Vol. 2021-December (pp. 6 pages). online: IEEE.
DOI Scopus4

Year Citation
2025 Simpson, L., Lawrie, T., Muller, Q., & Adesso, G. (2025). Towards Analogue Wave Computing via Quantum Graph Theory.

ANA Partnership Grant Scheme: Physics-Informed Neural Networks for Quantum Graph-Based Medical Imaging Devices

  • Autonomous Systems: Marker, Tutor, and Demonstrator
  • Control: Marker
  • Financial Modelling: Tools and Techniques: Marker and Tutor
  • Maths 1A: Marker
  • Maths 1B: Marker
  • Vector Calculus and Electromagnetics: Marker
  • Mathematics for Data Science: Marker
  • Artificial Intelligence: Tutor
  • Algorithm and Data Structure Analysis: Tutor
  • Differential Equations for Engineers: Marker
  • Introduction to Financial Mathematics: Marker
  • Applications of Quantitative Methods in Finance: Marker

Date Role Research Topic Location Program Supervision Type Student Load Student Name
2023 - 2024 Principal Supervisor Explainable AI for Graph-based Device Characterisation University of Adelaide Summer Vacation Research Project Other Full Time Gerald Freislich

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