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Mr Nic Kuo

Mr Nic Kuo

Research Fellow
Medicine & Health
Centre for Big Data Research in Health

Nicholas I-Hsien Kuo (Nic) has a Masters in Science (Applied Mathematics + Statistics) at the UoA,
and a Doctor of Philosophy (Computer Science) at the Australian National University (ANU).

He joined the Centre for Big Data Research for Health (CBDRH) in 2021
and is currently working with Professor Louisa Jorm and Dr. Sebastiano Barbieri.
E-mail
n.kuo@unsw.edu.au
Location
Level 2, AGSM Building (G27), Gate 11, Botany St, UNSW Sydney Campus, Botany St, Kensington NSW 2052
  • Journal articles | 2023
    Kuo NIH; Garcia F; Sönnerborg A; Böhm M; Kaiser R; Zazzi M; Polizzotto M; Jorm L; Barbieri S, 2023, 'Generating synthetic clinical data that capture class imbalanced distributions with generative adversarial networks: Example using antiretroviral therapy for HIV', Journal of Biomedical Informatics, 144, http://dx.doi.org/10.1016/j.jbi.2023.104436
    Journal articles | 2022
    Kuo NIH; Polizzotto MN; Finfer S; Garcia F; Sönnerborg A; Zazzi M; Böhm M; Kaiser R; Jorm L; Barbieri S, 2022, 'The Health Gym: synthetic health-related datasets for the development of reinforcement learning algorithms', Scientific Data, 9, pp. 693, http://dx.doi.org/10.1038/s41597-022-01784-7
  • Preprints | 2023
    Kuo NI-H; Jorm L; Barbieri S, 2023, Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models, , http://dx.doi.org/10.48550/arxiv.2303.12281
    Preprints | 2023
    Kuo NI-H; Perez-Concha O; Hanly M; Mnatzaganian E; Hao B; Di Sipio M; Yu G; Vanjara J; Valerie IC; de Oliveira Costa J; Churches T; Lujic S; Hegarty J; Jorm L; Barbieri S, 2023, Enriching Data Science and Healthcare Education: Application and Impact of Synthetic Datasets through the Health Gym Project (Preprint), , http://dx.doi.org/10.2196/preprints.51388
    Preprints | 2022
    Kuo NI-H; Garcia F; Sönnerborg A; Zazzi M; Böhm M; Kaiser R; Polizzotto M; Jorm L; Barbieri S, 2022, Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV, , http://dx.doi.org/10.48550/arxiv.2208.08655
    Preprints | 2022
    Kuo NI-H; Polizzotto MN; Finfer S; Garcia F; Sönnerborg A; Zazzi M; Böhm M; Jorm L; Barbieri S, 2022, The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms, , http://dx.doi.org/10.48550/arxiv.2203.06369
    Preprints | 2021
    Kuo NI-H; Harandi M; Fourrier N; Walder C; Ferraro G; Suominen H, 2021, Learning to Continually Learn Rapidly from Few and Noisy Data, , http://dx.doi.org/10.48550/arxiv.2103.04066
    Preprints | 2021
    Kuo NI-H; Polizzotto M; Finfer S; Jorm L; Barbieri S, 2021, Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project, , http://dx.doi.org/10.48550/arxiv.2112.03914
    Conference Papers | 2021
    Kuo NIH; Harandi M; Fourrier N; Walder C; Ferraro G; Suominen H, 2021, 'Plastic and stable gated classifiers for continual learning', in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 3548 - 3553, http://dx.doi.org/10.1109/CVPRW53098.2021.00394
    Preprints | 2020
    Kuo NI-H; Harandi M; Fourrier N; Walder C; Ferraro G; Suominen H, 2020, MTL2L: A Context Aware Neural Optimiser, , http://dx.doi.org/10.48550/arxiv.2007.09343
    Conference Papers | 2020
    Kuo NIH; Harandi M; Fourrier N; Walder C; Ferraro G; Suominen H, 2020, 'An Input Residual Connection for Simplifying Gated Recurrent Neural Networks', in Proceedings of the International Joint Conference on Neural Networks, http://dx.doi.org/10.1109/IJCNN48605.2020.9207238
    Conference Papers | 2020
    Kuo NIH; Harandi M; Fourrier N; Walder C; Ferraro G; Suominen H, 2020, 'M2SGD: Learning to learn important weights', in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 957 - 964, http://dx.doi.org/10.1109/CVPRW50498.2020.00126

Studied at the ANU under the Australian Government Research Training Program Domestic Scholarship.

My Research Interests
I always have a lot of interest in various disciplines of machine learning.
Currently, I am spending most of my time on the following 3 topics:
1) On Automating the Pipeline of Creating Synthetic Clinical Data,
2) On Processing Long Documents of Mixed Type Data, and
3) On the Theories of Continual Network Optimisation in a Low Resource Environment.

Extra Activities
I am a member of Dr. Oscar Perez Concha's Friday Machine Learning Club.
Most of our discussions in 2022 are / will be related to the latest developments of
Transformer-based neural network models for natural language processing.

My Teaching

I am working with my CBDRH colleagues in preparing Health Data Analytics (HDAT) course materials.

You will find me appearing on
HDAT9000 -- Clinical Artificial Intelligence -- on the Causal Inference lectures; and
HDAT9510 -- Machine Learning II -- on the RNN lectures.