Madeleine Cochrane

Madeleine Cochrane

Higher Degree by Research Candidate

School of Computer and Mathematical Sciences

Faculty of Sciences, Engineering and Technology

I am a PhD student with a background in AI and robotics and a particular interest in medical machine learning.I received my undergraduate degrees in Mechatronics Engineering and Computer Science from Swinburne University of Technology in 2018. During my studies I undertook placements with CSIRO and the Defence Science and Technology Group (DSTG) and worked on a range of projects including a robotic rehabilitation system for stroke patients, navigation algorithms for unmanned aerial vehicles operating in cluttered environments, a biomimetic autonomous underwater vehicle and a 3D simulation environment for autonomous vehicles. Following my degree, I worked as a researcher with DSTG. My work there focused on the development of multi-agent task allocation algorithms for ground based vehicles. I began my PhD at the University of Adelaide in 2023, driven by a desire to learn more about machine learning and the ways it can be used to improve healthcare outcomes.

My current research is focused on using machine learning to analyse biomedical signals to aid in disease monitoring and diagnosis. 

The core part of my research is part of a multidisciplinary effort across the Australian Institute of Machine Learning and the Institute of Photonics and Advanced Sensing to develop a proof-of-concept breath analysis system to aid in disease diagnosis. Our system combines machine learning and molecular spectroscopy with the aim of providing rapid and accurate information on the volatile organic compounds contained in a patient's breath. My work focuses on developing machine learning models to estimate the concentration levels of molecules in breath. The overarching goal of my PhD is to develop a system that can distinguish between signs of pneumonia and heart failure in patients experiencing acute breathlessness.

I also use machine learning to analyse ECG signals. This work focuses on the estimation of ejection fraction in order to support early detection and monitoring of heart failure.

  • Journals
    Year Citation
    2025 Cochrane, M., Scholten, S. K., Perrella, C., van den Hengel, A., Dholakia, K., Luiten, A. N., . . . Verjans, J. W. (2025). CO<sub>2</sub> Isotopologue Quantification Using Direct Frequency Comb Spectroscopy and Machine Learning. ACS OMEGA, 10(43), 51443-51454.
    DOI
    2021 Fisher, A., Cannizzaro, R., Cochrane, M., Nagahawatte, C., & Palmer, J. L. (2021). ColMap: A memory-efficient occupancy grid mapping framework. Robotics and Autonomous Systems, 142, 12 pages.
    DOI Scopus53 WoS33
    2021 Elfeky, E. Z., Elsayed, S., Marsh, L., Essam, D., Cochrane, M., Sims, B., & Sarker, R. (2021). A Systematic Review of Coevolution in Real-Time Strategy Games. IEEE ACCESS, 9, 136647-136665.
    DOI WoS4

  • Conference Papers
    Year Citation
    2022 Cochrane, M., Coyle, A., & Zamani, M. (2022). Value Based Analysis of a Counter UAS Problem. In Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence Ssci 2022 (pp. 588-595). IEEE.
    DOI
    2022 Elfeky, E., Cochrane, M., Marsh, L., Elsayed, S., Sims, B., Crase, S., . . . Sarker, R. (2022). Coevolutionary Algorithm for Evolving Competitive Strategies in the Weapon Target Assignment Problem. In Proceedings of the 2022 6th International Conference on Intelligent Systems, Metaheuristics &amp; Swarm Intelligence (pp. 9-18). ACM.
    DOI
    2022 Elfeky, E., Cochrane, M., Crase, S., Elsayed, S., Sims, B., Essam, D., & Sarker, R. (2022). Coevolution with Danger Zone Levels Strategy for the Weapon Target Assignment Problem. In 2022 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 596-603). IEEE.
    DOI
    2020 Marsh, L., Cochrane, M., Lodge, R., Sims, B., Traish, J., & Xu, R. (2020). Autonomous Target Allocation Recommendations. In 2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) (pp. 1403-1410). ELECTR NETWORK: IEEE.
    DOI WoS2
    2020 Cochrane, M., & Hunjet, R. (2020). A Multi-Armed Bandit Strategy for Countermeasure Selection. In 2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) (pp. 2510-2515). ELECTR NETWORK: IEEE.
    DOI WoS1
    2018 Hunjet, R., Fraser, B., Stevens, T., Hodges, L., Mayen, K., Barca, J. C., . . . Palmer, J. L. (2018). Data Ferrying with Swarming UAS in Tactical Defence Networks. In 2018 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) (pp. 6381-6388). Brisbane, AUSTRALIA: IEEE COMPUTER SOC.
    DOI WoS9
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