📡〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️📡 Reinforcement Learning Agents 🤝 Wireless Communication Our paper "Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning" just won 🥇best student paper award at WiOpt 2025. (The research started under my Masters Thesis at Columbia Under Igor Katoda and Gil Zussman and was more recently continued by Pedro Botelho, Yubo Zhang and Igor at Northwestern). Problem: When numerous wireless devices compete for limited bandwidth, like phones📱 at a music festival, communication becomes chaotic ❌ 🛜 . Our paper presents a new approach where devices independently discover how to avoid interference and equitably share the spectrum. Key technical contributions include: • A novel distributional Multi-Agent Reinforcement Learning (MARL) architecture using a Likelihood Hysteretic Implicit Quantile Network (LH-IQN) for decentralized cooperation 🤝. This architecture, a core part of my thesis, allows each agent to learn a distribution of possible outcomes, leading to better coordination. • The incorporation of dynamic risk control to facilitate coordination by encouraging agents to attempt transmissions and learn effective sharing strategies 💡. In situations with limited spectrum, agents might become too cautious. Our method dynamically adjusts their willingness to take risks to ensure they actively try to transmit and discover how to share the resources efficiently. • Fairness-driven reward structures that look at the recent history of each agent to promote equitable spectrum sharing. Instead of just rewarding successful transmissions, our reward system encourages agents to share the spectrum more evenly over time, without any central coordination.

📡〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️〰️📡 Reinforcement Learning Agents 🤝 Wireless Communication Our paper "Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning" just won 🥇best student paper award at WiOpt 2025. (The research started under my Masters Thesis at Columbia Under Igor Katoda and Gil Zussman and was more recently continued by Pedro Botelho, Yubo Zhang and Igor at Northwestern). Problem: When numerous wireless devices compete for limited bandwidth, like phones📱 at a music festival, communication becomes chaotic ❌ 🛜 . Our paper presents a new approach where devices independently discover how to avoid interference and equitably share the spectrum. Key technical contributions include: • A novel distributional Multi-Agent Reinforcement Learning (MARL) architecture using a Likelihood Hysteretic Implicit Quantile Network (LH-IQN) for decentralized cooperation 🤝. This architecture, a core part of my thesis, allows each agent to learn a distribution of possible outcomes, leading to better coordination. • The incorporation of dynamic risk control to facilitate coordination by encouraging agents to attempt transmissions and learn effective sharing strategies 💡. In situations with limited spectrum, agents might become too cautious. Our method dynamically adjusts their willingness to take risks to ensure they actively try to transmit and discover how to share the resources efficiently. • Fairness-driven reward structures that look at the recent history of each agent to promote equitable spectrum sharing. Instead of just rewarding successful transmissions, our reward system encourages agents to share the spectrum more evenly over time, without any central coordination.

Wireless Optogenetic Neural Dust for Deep Brain Stimulation (2016) #eHealth Jornet / Balasubramaniam proceedings.com/content/032/…