Francis Y. Yan

Francis Y. Yan

Senior Researcher
Microsoft Research

  • email
  • place Redmond, WA

© 2020 Designed by Francis Y. Yan

I am a computer scientist at Microsoft Research (Redmond) and the Office of the CTO, Azure for Operators. I created Puffer, a video streaming site with 200,000+ users and an open research platform widely used in academia.

My current research interests span:

  • Intelligent video systems (conferencing and streaming)
  • Practical ML for networked systems

Bio: I completed my Ph.D. in computer science at Stanford University, advised by Keith Winstein and Philip Levis. My dissertation was on the development of platforms and algorithms to achieve practical reinforcement learning on the internet. Before that, I graduated from Tsinghua University, where I received a B.S. in computer science from the Yao Class and a B.A. in economics. I also studied at MIT during my undergraduate years. My work has received the IRTF Applied Networking Research Prize, the USENIX NSDI Community Award, the USENIX ATC Best Paper Award, and the APNet Best Paper Award.

News
May 2023
Teal and Slingshot have been accepted into SIGCOMM 2023!
Apr 2023
We welcome PhD students and postdocs working in the area of ML and systems to apply to the inaugural Machine Learning and Systems Rising Stars 2023! Applications are due on April 21, 2023.
Mar 2023
Co-chairing ACM/IRTF Applied Networking Research Workshop (ANRW) 2023 with Maria Apostolaki (Princeton). ANRW presents a fantastic opportunity for researchers to exchange ideas with standards developers. We are eager to receive your submissions!
Feb 2023
I just open-sourced Ringmaster, a videoconferencing research platform as part of our Tambur paper at NSDI '23. Ringmaster aims to be a readable, extensible, and research-friendly replacement for WebRTC.
Jan 2023
Soudeh Ghorbani (JHU) and I are thrilled to announce that the poster session is making a comeback at NSDI '23 after a three-year break! Please review the Call for Posters and submit your abstracts by Feb 27, 2023.
Dec 2022
A preview of my recent work on ML for networking is now available at arXiv: Teal (learning-accelerated traffic engineering) and Autothrottle (learning-assisted microservice autoscaling)!
Dec 2022
Our paper “Tambur: Efficient loss recovery for videoconferencing via streaming codes” has been (pre-)accepted into NSDI 2023!
Nov 2022
Serving on the PC of CoNEXT 2023.
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Recent Research
Zhiying Xu, Francis Y. Yan, Rachee Singh, Justin T. Chiu, Alexander M. Rush, Minlan Yu
To appear at SIGCOMM, September 2023
Teal is a deep learning-based traffic engineering (TE) scheme that accelerated the TE optimization on large WANs by several orders of magnitude while achieving near-optimal traffic allocation.
Zibo Wang, Pinghe Li, Chieh-Jan Mike Liang, Feng Wu, Francis Y. Yan
arXiv:2212.12180 (preprint), May 2023
Autothrottle is a bi-level learning-assisted resource management framework that autoscales CPUs for microservice applications with latency SLOs. It uses a lightweight learned controller at the application level to assist agile per-microservice controllers, practically saving CPU resources without violating SLOs.
Michael Rudow, Francis Y. Yan, Abhishek Kumar, Ganesh Ananthanarayanan, Martin Ellis, K.V. Rashmi
USENIX Symposium on Networked Systems Design and Implementation (NSDI), April 2023
Tambur is a new approach to forward error correction (FEC) for videoconferencing built upon streaming codes and machine learning. Tambur reduced decoding failures while consuming less bandwidth for redundancy.
Zhengxu Xia*, Yajie Zhou*, Francis Y. Yan, Junchen Jiang (*equal contribution)
Proceedings of the ACM Special Interest Group on Data Communication (SIGCOMM), August 2022
Genet is a novel training framework that enhances the performance and generalization of reinforcement learning (RL) algorithms in networking. Genet builds on curriculum learning with judicious use of rule-based baselines. It substantially improves the performance and generalization of simulation-trained RL algorithms under unseen workloads and in real environments.
Jeongyoon Eo, Zhixiong Niu, Wenxue Cheng, Francis Y. Yan, Rui Gao, Jorina Kardhashi, Scott Inglis, Michael Revow, Byung-Gon Chun, Peng Cheng, Yongqiang Xiong
Asia-Pacific Workshop on Networking (APNet), July 2022
emoji_events Best Paper Award
OpenNetLab is an open platform for training, validating, and evaluating RL-based congestion-control (bandwidth estimation) algorithms for real-time communications (RTC) such as videoconferencing. It has successfully aided the development of novel RL-based congestion-control algorithms for RTC during our Grand Challenge hosted at ACM MMSys '21.
Francis Y. Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong, Keyi Zhang, Philip Levis, Keith Winstein
USENIX Symposium on Networked Systems Design and Implementation (NSDI), February 2020
website talk slides code wikipedia
We built Puffer, a free, publicly accessible website that live-streams television channels and operates as a randomized experiment of adaptive bitrate (ABR) algorithms. As of June 2020, Puffer has attracted 120,000 real users and streamed 60 years of video across the internet. Using Puffer, we developed an ML-based ABR algorithm, Fugu, that robustly outperformed existing schemes by learning in situ, on real data from its actual deployment environment.
Francis Y. Yan, Jestin Ma, Greg D. Hill, Deepti Raghavan, Riad S. Wahby, Philip Levis, Keith Winstein
USENIX Annual Technical Conference (ATC), July 2018
website talk slides code
Pantheon is a “training ground” for congestion-control research and has assisted four schemes from other research groups in publishing at NSDI 2018 (Copa and Vivace), ICML 2019 (Aurora), and SIGCOMM 2020 (TCP-TACK). It also enabled our own ML-based congestion-control algorithm, Indigo, which was trained to imitate expert congestion-control algorithms we created in emulation and achieved good performance over the real internet.
Service
  • Program Committee Member, USENIX NSDI 2024
  • Program Committee Member, Machine Learning and Systems Rising Stars 2023
  • Program Committee Chair, ACM/IRTF Applied Networking Research Workshop 2023
  • Program Committee Member, IEEE ICNP 2023
  • Program Committee Member, ACM CoNEXT 2023
  • Award Committee Member, IRTF Applied Networking Research Prize 2023
  • Poster Chair, USENIX NSDI 2023
  • Program Committee Member, USENIX NSDI 2023
  • Networking Area Chair, Journal of Systems Research (2022–2023)
  • Award Committee Member, IRTF Applied Networking Research Prize 2022
  • External Reviewer, USENIX NSDI 2022
  • Editorial Board Member, Journal of Systems Research (2021–2022)
  • Challenge Chair, ACM MMSys 2021
  • Organizer, Grand Challenge on Bandwidth Estimation for Real-Time Communications, ACM MMSys 2021
  • Reviewer, IEEE/ACM Transactions on Networking (2019–)
  • Reviewer, ACM SIGCOMM Computer Communication Review (2019)