I am a computer science researcher at Microsoft Research in Redmond and the Office of the CTO, Azure for Operators. My research is primarily in networked systems, with a focus on enhancing them with practical machine learning (ML) algorithms. Video is a major thrust of my work owing to its synergy with ML.

Bio: 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, where he was 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.

News
Dec 2023 Grace has been accepted into NSDI 2024!
Nov 2023 Serving on the program committee of NSDI 2025.
Oct 2023 Serving on the program committee of SIGCOMM 2024.
July 2023 Autothrottle has been accepted into NSDI 2024!
May 2023 Teal and Slingshot have been accepted into SIGCOMM 2023!
Apr 2023 Served as NSDI 2023 Poster Co-Chair.
Recent Research
Zibo Wang, Pinghe Li, Chieh-Jan Mike Liang, Feng Wu, Francis Y. Yan
To appear in USENIX Symposium on Networked Systems Design and Implementation (NSDI), April 2024
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.
Zhiying Xu, Francis Y. Yan, Rachee Singh, Justin T. Chiu, Alexander M. Rush, Minlan Yu
Proceedings of the ACM Special Interest Group on Data Communication (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.
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
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
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 2025
  • Program Committee Member, ACM SIGCOMM 2024
  • Program Committee Member, Machine Learning and Systems Rising Stars 2024
  • Award Committee Member, IRTF Applied Networking Research Prize 2024
  • Steering Committee Member, ACM/IRTF Applied Networking Research Workshop (2024–2026)
  • 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 (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)