CEML · Center for Environmental Machine Learning

Machine learning for animals, sound, and changing seas.

CEML is an emerging independent lab focused on environmental machine learning: data curation for long-term ecological datasets, photo-ID, bioacoustics, and meta-learning that links metadata and context to downstream analysis.

Data, signals, and structure for real-world conservation questions.
Contact CEML Concept note (coming soon)
CEML is currently in its early formation stage. You may also encounter our work through platforms such as Finwave (finwave.io) for collaborative photo-ID and encounter management.
Initial themes
  • Focus areas
    Photo-ID and encounter management, bioacoustic analysis, and careful data curation for long-term environmental and animal datasets.
  • Methods
    Representation learning, co-occurrence and meta-learning, and ML systems that make metadata and context first-class citizens in ecological analysis.
Photo-ID & encounters Bioacoustics Data curation Co-occurrence Meta-learning
© CEML – Center for Environmental Machine Learning Environmental ML for animals, sound, and changing ecosystems.