<aside> <img src="/icons/link_gray.svg" alt="/icons/link_gray.svg" width="40px" /> 🌐 adrian.wilkinscaruana.com ✉️ [email protected] 📞 +61 407 381 557

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<aside> <img src="/icons/info-alternate_gray.svg" alt="/icons/info-alternate_gray.svg" width="40px" /> Adrian is an experienced machine learning researcher and practitioner. From MLOps to R&D, he brings industry and academic expertise to a broad range of data-driven projects.

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Education

Ph.D. in Computer Science

University of Technology Sydney Jan 2021 - Mar 2024  Thesis: Modelling population-level treatment patterns in patient-level administrative health records

B.Eng. in Mechatronic Engineering

University of Technology Sydney ****Mar 2014 - Jul 2019 Thesis: Hardware-implemented deep learning algorithms for quad-tree partitioning in HEVC

Talks

2022: Department of Health, Australian Government. Title: Abstract Pattern Recognition in Administrative Health Data

2022: Australasian Joint Conference on Artificial Intelligence (paper)

2021: Children’s Cancer Research Unit, Westmead Hospital. Title: Discovering Treatment Patterns in EHRs

2018: Australasian Conference on Robotics & Automation (Paper)

References

A comprehensive list of professional and academic references is available upon request.

Experience

Nearmap LTD | Machine Learning Engineer  Jan 2019 - Aug 2022, and Jul 2023 - Present

As a full-stack ML Engineer, I have contributed to all aspects of Nearmap’s AI capability, from model R&D to production system design and implementation. Specific contributions include:

• Design, training, and evaluation of deep neural networks
• Design of label sampling projects and A-B tests.
• Extensive production system engineering.
• Mentoring of junior data scientists and engineers.

Atlassian Corporation PLC | Machine Learning Scientist  Aug 2022 - Jul 2023

• I build machine learning models that analyse user and customer behavioural data to derive business and marketing insights.
• I co-develop, demonstrate, and present Atlassian’s internal ML tooling to help drive ML adoption and fluency within the company.

Freelance | Researcher, Lecturer, Writer

• Unbox Research [2022 - Present]: I translate emerging ML research into understandable blog posts. You can read them here.
• University of Technology Sydney [2021 - 2022]: Lecturing, tutoring, and admin for 32130 Fundamentals of Data Analytics.
• Big Sand [2021] is a sci-fi virtual band exploring new ways to interact with fans. I investigated how AI & LLMs fit into this goal.

Blackmagic Design PTY LTD | Research Engineer  Dec 2017 – Dec 2018

• I undertook ML R&D for real-time FPGA-based video encoding.
• Replaced rule-based, heuristic algorithms with faster, more precise neural network-based alternatives.
• Wrote and reviewed code for design and verification, and developed automated system integration processes.

Projects

• mustalgia.me: Last.fm music listening insights [ GitHub, blog post ]
• RefMan: A minimal Python-based reference manager [ GitHub, PyPI ]
• BibBib: A minimal Python-based BibTeX validator [ GitHub ]
• py_wlgk: Python Weisfeiler-Lehman graph kernel implementation [ GitHub ]

Skills

• AI / ML, Data Science, MLOps, software engineering, data visualisation
• Programming languages: Python, Bash, C/C++, SQL, JavaScript, VHDL, Haskell
• Packages: PyTorch, Scikit-Learn, TensorFlow, Pandas, Numpy, PySpark
• Tools: Git, Docker, Linux/Unix, Tmux, Databricks, AWS, Kubernetes, Kubeflow

Publications

Links: My arXiv papers, my ORCiD, and my Google Scholar profile.

1. A. Wilkins-Caruana, M. Bandara, K. Musial, D. Catchpoole, and P. J. Kennedy. Inferring actual treatment pathways from health records. Journal of Biomedical Informatics, 2023. https://doi.org/10.1016/j.jbi.2023.104554
2. A. Caruana, M. Bandara, K. Musial, D. Catchpoole, and P. J. Kennedy. Machine learning for administrative health records: A systematic review of techniques and applications. Artificial Intelligence in Medicine, 2023. https://doi.org/10.1016/j.artmed.2023.102642
3. A. Caruana, M. Bandara, D. Catchpoole, and P. J. Kennedy. Beyond topics: Discovering latent healthcare objectives from event sequences. In AI 2021: Advances in Artificial Intelligence, pages 368–380. Springer International Publishing, 2022. https://doi.org/10.1007/978-3-030-97546-3_30
4. A. Caruana and T. Vidal-Calleja. Very low complexity convolutional neural network for generating quadtree structures. Australasian Conference on Robotics and Automation, 2018.