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Eco-aware GNN Recommenders
In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Antonio discusses his research on "EcoAware Graph Neural Networks for Sustainable Rec...
About This Episode
Eco-aware GNN Recommenders is an episode from Data Skeptic by Kyle Polich. In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Antonio discusses his research...
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Episode Details
Published Aug 30, 2025, 44:42 long, audio available.
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Questions About This Episode
What is Eco-aware GNN Recommenders about?
In this episode of Data Skeptic, we dive into eco-friendly AI with Antonio Purificato, a PhD student from Sapienza University of Rome. Antonio discusses his research on "EcoAware Graph Neural Networks for Sustainable Recommendations" and explores how we can measure and reduce the environmental impact of recommender systems without sacrificing performance.
Where can I listen to Eco-aware GNN Recommenders?
You can listen to Eco-aware GNN Recommenders online on Radio and Podcast. Open the player on this page to stream the available audio.
Which podcast is Eco-aware GNN Recommenders from?
Eco-aware GNN Recommenders is an episode from Data Skeptic by Kyle Polich.
How long is this episode?
This episode is 44:42 long.
When was this episode published?
This episode was published on Aug 30, 2025.
Can I save Eco-aware GNN Recommenders for later?
Yes. Use the heart button on the episode page to add it to your favorite episodes list.
Are there related episodes from Data Skeptic?
Yes. This page shows related episodes from Data Skeptic when more episodes are available from the podcast feed.
Quick Answers About This Episode
Where can I listen to Eco-aware GNN Recommenders?
You can listen to Eco-aware GNN Recommenders on this page when the episode audio is available from the podcast feed.
Which podcast is this episode from?
Eco-aware GNN Recommenders is from Data Skeptic by Kyle Polich.
What are the episode details?
Published Aug 30, 2025 and 44:42 long