Joint Computer Science and Electrical and Computer Engineering Seminar
Dr. Francis Y. Yan
Senior Researcher, Microsoft Research
Wednesday, April 10, 2024, 10:00-11:00 am
Online via Zoom
Title: Practical Machine Learning for
Networked Systems
Abstract: The growing complexity
and heterogeneity of networked systems have spurred a plethora of machine
learning (ML) solutions, each promising a tantalizing improvement in
performance. However, their path to real-world adoption is fraught with
obstacles due to concerns from system operators about ML's generalization,
transparency, robustness, and efficiency.
My research takes a holistic approach to enabling practical
ML for networked systems: 1) building open research platforms to lay the
foundation for ML-based algorithms; 2) complementing ML with classical
techniques (e.g., time-tested heuristics, control algorithms, or optimization
methods) for enhanced deployability; and 3) validating ML-augmented methods
through extensive empirical evidence gathered from real users or production
systems. In this talk, I will demonstrate this research approach using three studies:
Puffer/Fugu learns to adapt video bitrate in situ on a live streaming service
we developed (with over 280,000 users to date), Autothrottle learns to assist
resource management for cloud microservices, and Teal learns to accelerate
traffic engineering on wide-area networks. Finally, I will conclude by
outlining my research agenda for further pushing the boundaries of practical ML
in networked systems.
Francis Y. Yan is a
Senior Researcher at Microsoft Research in Redmond and the Office of the CTO,
Azure for Operators. His research is primarily in networked systems, with a
focus on enhancing them with practical machine learning algorithms. Francis
received his Ph.D. in computer science from Stanford University, advised by
Keith Winstein and Philip Levis. Before that, he completed his undergraduate
studies at Tsinghua University (Yao Class) and MIT. His work has engaged
hundreds of thousands of real users and also found wide use in academia, aiding
researchers in publishing many papers at top-tier conferences. He is a
recipient of an IRTF Applied Networking Research Prize, a USENIX NSDI Community
Award, a USENIX ATC Best Paper Award, and an APNet Best Paper Award.
