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GSoC 2024 Project Ideas

henry senyondo edited this page Feb 6, 2024 · 8 revisions

Please ask questions through issues on the respective project's repo.

Tags available @henrykironde, @bw4sz, @ethanwhite,

  • Preferred names (Henry, Ben, Ethan)
  • Preferred_greeting (Hi|Hello|Dear|Thanks|Thank you [First_name])

The code of conduct should be your first read.

Tittle

Rationale

Approach

Source Code: deepForest Associated Code:

Degree of difficulty

  • Intermediate, long (350 hours)

Skills:

  • git/GitHub
  • Machine learning
  • Software testing
  • Python and Python package deployment

Expected outcomes

Mentors

  • @bw4sz
  • @henrysenyondo
  • @ethanwhite

Efficient Parallel Computing for Model Fitting and Prediction in Portalcasting R package

Rationale

Portalcasting, an open-source R package, aids in ecological forecasting of biodiversity within a long-term ecological research program focused on studying desert biodiversity over 45 years. The package facilitates automated data integration and modular models for generating forecasts across various ecological outcomes. Presently, the forecasting system executes numerous forecasts sequentially, and this project aims to parallelize the codebase, enabling concurrent execution on multiple cores, both on individual machines and HPCs.

Approach

Portalcasting relies on supporting packages like PortalData and Portalr. PortalData contains all Portal project data, while Portalr offers functions for summarizing this data. The portalcasting package integrates PortalData and Portalr into a streamlined pipeline, used by portal-forecasts. The forecast results are displayed on the interactive dashboard. Currently, the forecast takes about four hours, with 98% of the time consumed by the portalcast() function. Our aim is to reduce the time by enabling parallel execution of the function, considering the shared data used by all models.

Source Code: https://github.com/weecology/portalcasting

Degree of difficulty

  • Intermediate, short (175 hours)

Skills:

  • R
  • Parallel programming in R
  • git/GitHub
  • Software Development

Expected outcomes

  • An optimized parallel program designed to significantly decrease execution time.

Mentors

  • @henrysenyondo
  • @ethanwhite