Gaurangi Anand

Gaurangi Anand

Australian Institute for Machine Learning - Projects

Faculty of Sciences, Engineering and Technology


As a postdoctoral research fellow at the Australian Institute for Machine Learning, I will be leveraging machine learning techniques to determine the probability distribution of collaboratively interacting entities in complex, noise-laden datasets. Previously, during my postdoctoral research at CSIRO, I utilized Graph Neural Networks (GNNs) to predict relationships between species and chemicals, tackling the challenges posed by diverse and varying experimental conditions. During my PhD at the Queensland University of Technology, Brisbane, I developed effective unsupervised representation learning methods for time series and trajectory data. This diverse experience equips me with a robust foundation to address diverse, complex, data-intensive scientific problems with innovative solutions.

  • Appointments

    Date Position Institution name
    2021 - 2024 Postdoctoral Research Fellow Commonwealth Scientific and Industrial Research Organisation
  • Journals

    Year Citation
    2024 Anand, G., Koniusz, P., Kumar, A., Golding, L. A., Morgan, M. J., & Moghadam, P. (2024). Graph neural networks-enhanced relation prediction for ecotoxicology (GRAPE). JOURNAL OF HAZARDOUS MATERIALS, 472, 11 pages.
    DOI
    2021 Anand, G., & Nayak, R. (2021). DeLTa: Deep local pattern representation for time-series clustering and classification using visual perception. Knowledge-Based Systems, 212, 106551.
    DOI
    2020 Garg, S., Harwood, B., Anand, G., & Milford, M. (2020). Delta Descriptors: Change-Based Place Representation for Robust Visual Localization. IEEE Robotics and Automation Letters, 5(4), 5120-5127.
    DOI Scopus31 WoS15
  • Conference Papers

    Year Citation
    2020 Anand, G., & Nayak, R. (2020). Unsupervised Visual Time-Series Representation Learning and Clustering. In Unknown Conference (pp. 832-840). Springer International Publishing.
    DOI
    2018 Anand, G., & Nayak, R. (2018). Contextual Anomaly Detection in Spatio-Temporal Data using Locally Dense Regions. In 2018 IEEE 30TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI) (pp. 963-967). GREECE, Volos: IEEE.
    DOI
  • Theses

    Year Citation
    2021 Anand, G. (2021). Unsupervised visual perception-based representation learning for time-series and trajectories. (PhD Thesis, Queensland University of Technology).

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