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Email

sachith.seneviratne@unimelb.edu.au

Credentials


Position
Research Fellow in Computer Vision and Health-Thud
Faculty of Architecture, Building and Planning - Centres and Institutes
Education
Doctorate
Monash University
Bachelors Degree (Honours)
University of Moratuwa
ORCID

0000-0001-9094-2736

Dr Sachith Seneviratne

Research Fellow in Computer Vision and Health-Thud
Faculty of Architecture, Building and Planning

44 Scholarly works
0 Projects

HIGHLIGHTS

  • 2026

    Journal article

    AniFaceDiff: Animating stylized avatars via parametric conditioned diffusion models
    DOI: 10.1016/j.patcog.2025.112017
  • 2025

    Journal article

    Subgrid informed neural networks for high-resolution flood mapping
    DOI: 10.1016/j.jhydrol.2025.133329
  • 2025

    Journal article

    Physics-Informed Generative AI for High-Resolution Flood Mapping
    DOI: 10.5194/egusphere-egu25-9285
  • 2025

    Journal article

    Collecting population-representative bike-riding GPS data to understand bike-riding activity and patterns using smartphones and Bluetooth beacons
    DOI: 10.1016/j.tbs.2024.100919
  • 2025

    Journal article

    Cycling Route Choice Preferences: A Taste Heterogeneity and Exogenous Segmentation Analysis Based on Age, Gender, Geller Typology, and E-Bike Use
    DOI: 10.1016/j.tra.2025.104679
  • 2025

    Journal article

    Effects of city design on transport mode choice and exposure to health risks during and after a crisis: a retrospective observational analysis
    DOI: 10.1016/S2542-5196(25)00088-9
  • 2024

    Journal article

    A comparison of content from across contemporary Australian population health surveys
    DOI: 10.1016/j.anzjph.2024.100152
Sachith Seneviratne

RECENT SCHOLARLY WORKS

  • 2024

    Journal article

    GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations
    DOI: 10.1609/aaai.v38i13.29396
  • 2024

    Conference Proceedings

    Extending global-local view alignment for self-supervised learning with remote sensing imagery
    DOI: 10.1109/CVPRW63382.2024.00251
  • 2024

    Conference Proceedings

    Modelling link-level bike riding volumes in Greater Melbourne

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