Self-Driving Networks

The long-term research goal for Systems and Networking Lab (SNL) is to develop self-driving networks that leverage advances in different research areas (i.e., networking, machine learning, security, and computer architecture) to address the digital inequity issues. Specifically, our goal is to build self-driving last-mile networks (e.g., cable, mobile, satellite, etc.) that can lower the cost of operating highly available, reliable, performant, and secure network connectivity, highly desired for under-served communities.

To this end, our group explores how we can (1) enable accurate and flexible (streaming) analytics over network data at scale; (2) lower the threshold to curate high-quality datasets for different learning problems from diverse network environments at scale; (3) develop generalizable and robust learning models that can both accurately assess the network’s state and take effective actions to keep networks performant and secure; and (4) establish trust in ML-based artifacts so network operators feel confident enough to relinquish control to these artifacts in production settings.

Some of the recently published works that provide a sample of some of the ongoing activities at SNL:


The research in my group is funded by National Science Foundation (NSF) and different network service providers (Verizon Innovations, ViaSat) and vendors (Intel).

You can find more details about some of the funded projects here: