Project Description

A growing concern within today's networking community is that with the proliferation of Artificial Intelligence/Machine Learning (AI/ML) techniques, a lack of access to real-world production networks is putting academic researchers at a significant disadvantage. Indeed, compared to a select few research groups in industry that can leverage access to their global-scale production networks in their data-driven efforts to develop and evaluate learning models, academic researchers not only struggle to get their hands on real-world datasets but find it almost impossible to adequately train and assess their learning models under realistic conditions.

In this project, we instrument the campus network at UCSB to (i) serve as unique sources for some of the rich data that will enable these researchers to influence or advance the current state-of-the-art in AI/ML for networking and (ii) also function as much-needed test beds where newly developed AI/ML-based tools can be evaluated or "road-tested" prior to their actual deployment in the production network. 

Team Members

  • Sam Liang
  • Ethan Wu
  • Mateo Wang

Professor and Mentors

  • Prof. Arpit Gupta
  • Sanjay Chandrashekaran

Meeting Time

  • Research group meeting
    • Time and Location: Tuesdays 9 AM at HFH
  • Reading group
    • Time and Location: Fridays 12:30 PM -  2PM, TBD
  • ERSP meeting with central mentors (zoom for the first two weeks, in person after that)
    • Chinmay: Fridays 6PM - 6:30PM
    • Diba: TBD

Links to Proposals and Presentation

  • Proposal (first draft): link
  • Proposal (after peer review): link
  • Final proposal (after instructor feedback): link
  • Final presentation: link

Individual Logs

Peer Review

Project Documentation and Resource