Abstract: Artificial Intelligence has quickly become a powerful tool for addressing complex systems problems, and networking has been no exception. Yet, the journey from promising idea to practical impact often reveals unexpected challenges and deeper insights. This reflects lessons learned from applying AI to networking problems, drawing on experiences from our Maestro and Wixor projects. These efforts tackled issues such as Wi-Fi QoE orchestration and cellular (5G) resource scheduling, showing both the promise and the limits of AI in networked systems. Several themes emerged across these projects: the critical role of formulating the right learning problem, the tradeoffs between model complexity and system usability, and the value of embedding domain knowledge into AI-driven solutions. Along the way, we also discovered that applying AI to networking is not just about better algorithms—it can reshape how we think about networks themselves. This talk will take a step back to distill these experiences into broader reflections: when does AI truly help in networking, what pitfalls should we anticipate, and how can we design systems that are both intelligent and trustworthy?
Co-sponsored by: Rice University ECE and IEEE Student Branch
Speaker(s): Puneet Sharma
Agenda:
Presentation at 12:00 to 1:00 pm CDT at O'Connor Building for Engineering and Science, Room 406 at Rice University
Room: 406, Bldg: O'Connor Building for Engineering and Science, Rice University, 6100 Main Street, Houston, Texas, United States, 77005