Applied ML
Machine Learning in Oceanography: Beyond the Surface
Exploring how deep learning models are revolutionizing our understanding of marine ecosystems and physical oceanography.
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J. Francisco Avilés Blog & Field Notes
Technical write-ups, research reflections, and updates from the laboratory.
Opinion
Bridging the Gap Between Computer Science and Biology
Why interdisciplinary collaboration is no longer optional in the age of generative AI and large language models.
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J. Francisco Avilés Applied ML
Open Science in the AI Era: Building the Infrastructure We Need
A look into the necessity of open formats like SciMD to ensure scientific reproducibility when using AI systems.
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J. Francisco Avilés Field Notes
Real-World Challenges of Applied ML in Ecology
Tackling sparse datasets, noisy labels, and extreme class imbalances in biodiversity monitoring projects.
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J. Francisco Avilés Applied ML
The Future of Biodiversity Monitoring using Computer Vision
How automated acoustic monitoring and edge computer vision are scaling our capacity to track ecosystem health.
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J. Francisco Avilés 
Opinion
¿Por qué ANA Research?
El machine learning y la ciencia son dos campos que deben ser usados en conjunto, el requerimiento del analisis y modelado de grandes cantidades de datos hace que sea necesario el uso de modelos de inferencia para interpretar estos datos.
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J. F. Avilés Applied ML
Why Applied ML Matters More Than Ever
Moving beyond benchmarks to solve tangible problems requires a fundamental shift in how we approach model evaluation.
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Juan Perez