Deep Dive into Networked AI

Explore the cutting-edge of artificial intelligence (AI), machine learning, optimization, signal processing, wireless communications, networking, and broader networked systems. Join us as we dive deep into frontier research papers, uncover groundbreaking innovations, and discuss the possibilities shaping the future of technology and science.

Deep Dive into Networked AI is a technology podcast hosted by NetAIStudio, with 10 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

NetAIStudio hosts Deep Dive into Networked AI, a technology show with 10 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #10

  [Deep Dive] AirGNN: Graph Neural Networks over the air for Wireless Networks

  Feb 9, 202517 minS1

  <p>In this episode, we explore the nuances and key considerations of implementing graph neural networks as decentralized applications in wireless networks, such as source localization, multi-robot flocking, and wireless channel management — a core theme of this podcast, especially in this season. This discussion is based on a journal paper published in IEEE Transactions on Signal Processing, authored by Zhan Gao and Deniz Gündüz.</p><p><br></p><p>Generated using NotebookLM from Google, this podcast highlights the key findings and implications of this research.</p><p>🎧 Read the paper here: [IEEE TSP](https://ieeexplore.ieee.org/document/9042352)</p><p>📷...

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  Episode #9

  [Deep Dive] GLANCE: Graph-based Learnable Digital Twin for Wireless Networks

  Jan 31, 202517 minS1

  <p>In this episode, we dive into the applications of graph neural networks as a learnable digital twin of network simulators, which can accelerate network optimization by its fast and differentiable prediction of networking key performance indicators (KPIs). This episode is based on a preprient authored by Boning Li, et al. </p> <p><br></p> <p>Generated using NotebookLM from Google, this podcast highlights the key findings and implications of this research.</p> <p>🎧 Read the paper here: [arXiv]</p> <p>📷 Cover Image Source: imagine.art, Microsoft Designer</p> <p>🎵 BGM: Artlist.io</p> <p>🛠️ Credits: NotebookLM by Google</p>

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  Episode #8

  [Deep Dive] Opportunities and Challenges of Graph Neural Networks in Electrical Engineering

  Jan 24, 202526 minS1

  <p>In this episode, we dive into a survey paper of the applications of graph neural networks in electrical engineering. This episode is based on our recent publication in Nature Review Electrical Engineering. </p> <p><br></p> <p>Generated using NotebookLM from Google, this podcast highlights the key findings and implications of this research.</p> <p>🎧 Read the paper here: [Online]</p> <p>📷 Cover Image Source: imagine.art, Microsoft Designer</p> <p>🎵 BGM: Artlist.io</p> <p>🛠️ Credits: NotebookLM by Google</p>

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  Episode #7

  [Deep Dive] Fully Distributed Online Training of GNNs in Networked Systems

  Jan 17, 202519 minS1

  <p>In this episode, we dive into fully distributed online training of graph neural networks deployed in networked systems. This episode is based on our recent preprint by Rostyslav Olshevskyi from Rice University. </p> <p><br></p> <p>Generated using NotebookLM from Google, this podcast highlights the key findings and implications of this research.</p> <p>🎧 Read the paper here: [Preprint]</p> <p>📷 Cover Image Source: imagine.art, Microsoft Designer</p> <p>🎵 BGM: Artlist.io</p> <p>🛠️ Credits: NotebookLM by Google</p>

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  Episode #6

  [Deep Dive] Learning Decentralized Wireless Resource Allocations with Graph Neural Networks

  Jan 10, 202519 minS1

  <p>In this episode, we dive into the combination of graph neural networks and unsupervised primal-dual learning, a model-free approach to scalable, intelligent wireless resource allocations. This episode is based on two papers published in IEEE Transactions on Signal Processing by Zhiyang Wang, Mark Eisen, and Alejandro Ribeiro. </p> <p><br /></p> <p><br /></p> <p>Generated using NotebookLM from Google, this podcast highlights the key findings and implications of this research.</p> <p>🎧 Read the papers here: [Eisen 2019], [Wang 2022]</p> <p>📷 Cover Image Source: imagine.art, Microsoft Designer</p> <p>🎵 BGM: Artlist.io</p> <p>🛠️ Credits: NotebookLM by Google</p> <p><br /></

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