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Achievements

Achievement: YOLOAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx2

Achievements

Achievement: YOLOAchievement: Arctic Code Vault ContributorAchievement: Pull Sharkx2

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nannau/README.md

Untitled design

About me

  • Scientist + developer interested in high-performance computing and scientific software systems
  • I specialize in Python, ML, and designing complex data science applications and pipelines
  • MSc in atmospheric sciences, and BSc in physics + astrophysics
  • Interested in scaling ML pipelines (multi-GPU/multi-node with Lightning AI, PyTorch, MLFlow, Comet ML)

Publications

Climate Science & Machine Learning

  • Annau et al. (2023) - Algorithmic hallucinations of near-surface winds: Statistical downscaling with generative adversarial networks to convection-permitting scales Artificial Intelligence for the Earth Systems, 2(4), e230015. DOI: 10.1175/AIES-D-23-0015.1
  • Climpyrical, scientific software computing National Building Code of Canada design values
  • Design Value Explorer Web application for visualizing and downloading design value fields and tables.
  • R Package ‘ClimDown’

Computational & Data-Driven Astronomy

  • Herwig et al. (2018) - Cyberhubs: Virtual research environments for astronomy The Astrophysical Journal Supplement Series, 236(1), 2. DOI: 10.3847/1538-4365/aabfe7

  • Monthly Notices of the Royal Astronomical Society, 503(1), 176–199. Thomas et al. (2019) - Dwarfs or giants? stellar metallicities and distances from ugrizg multiband photometry DOI: 10.1093/mnras/stab499

  • The Astrophysical Journal, 886(1), 10. Thomas et al. (2020) - VizieR Online Data Catalog: Dwarfs or giants? Stellar metallicities & distances (Thomas+, 2019) VizieR Online Data Catalog, J–ApJ. DOI: 10.3847/1538-4357/ab4a7f

Astrophysics and Cosmology

  • Higgs et al. (2021) - Solo dwarfs II: the stellar structure of isolated Local Group dwarf galaxies DOI: https://doi.org/10.1093/mnras/stab002

  • Christensen et al. (2017) - Solving the conundrum of intervening strong Mg II absorbers towards gamma-ray bursts and quasars Astronomy & Astrophysics, 608, A84. DOI: 10.1051/0004-6361/201731340

Conference Talks

  • ECMWF 2022 Machine Learning Workshop
  • CMOS 2022, Computational Methods Machine Learning and Model Development: Extreme Super‑Resolution and Downscaling of Wind Fields at Convection‑Permitting Scales
  • 6th Spatial Statistics Generative adversarial networks for super‑resolving near‑surface wind patterns
  • CMOS 2024 Leveraging AI for Enganced High-Resolution Regional Climate Modelling: ClimatExML: Designing AI Software for the Computational Demands of High‑Resolution Climate Models

Pinned Loading

  1. Annau-2023 Annau-2023 Public

    Completely frozen reproduction environment for Annau et al. 2023.

    Jupyter Notebook

  2. DoWnGAN DoWnGAN Public

    PyTorch/MLflow implementation of Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to perform single image super resolution (SISR) to downscale climate fields.

    Python 6 1

  3. climagination/nc2pt climagination/nc2pt Public

    Serializing NetCDF files for efficient use in deep learning pipelines.

    Python 7 1

  4. climagination/ClimatExML climagination/ClimatExML Public

    Jupyter Notebook 7 1

  5. pacificclimate/design-value-explorer pacificclimate/design-value-explorer Public

    Web application for interactively visualizing and downloading design value fields and tables.

    Python

  6. era_stream era_stream Public

    Simple cloud data loader implementation inspired by Earthmover

    Python