I am a Senior Researcher in the Mobility and Networking Research Group at Microsoft Research Redmond and the Office of the CTO, Azure for Operators. My research focuses on practical machine learning (ML) for networking, seeking to create ML algorithms that are deployable on real-world networked systems and build platforms for training and evaluating novel algorithms.

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 (RL) on the Internet, in the context of video streaming and congestion control. My work has received the Applied Networking Research Prize, the USENIX NSDI Community Award, and the USENIX ATC Best Paper Award.

Before that, I graduated from Tsinghua University, where I received a B.S. in computer science from Yao Class (founded by Turing Award laureate Andrew Chi-Chih Yao) and a B.A. in economics. I also studied at MIT in 2014. Here is my CV.

Prospective students: The internship positions of mine at MSR/Azure and MSRA have been filled, but please feel free to reach out if you are interested in joining a research project that applies ML/RL to systems and networking.

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
arrow_right 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
arrow_right Pantheon is a community “training ground” for Internet 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.
  • Applied Networking Research Prize (for Puffer), Internet Research Task Force (IRTF), 2021
  • USENIX NSDI Community Award (for Puffer), 2020
  • USENIX ATC Best Paper Award (for Pantheon), 2018
  • Award of Excellence, Stars of Tomorrow Internship Program, Microsoft Research, 2015
  • Outstanding Graduate of Tsinghua University (lone recipient at my institution), 2015
  • Outstanding Graduate of Beijing, China, 2015
  • Merrill Lynch Fellowship, Massachusetts Institute of Technology (declined), 2015
  • National Scholarship, China, 2014
  • Silver Prize, Yao Award, Tsinghua University, 2014
  • Tsinghua University Comprehensive Scholarship, 2013
  • National Endeavor Fellowship, China, 2012
  • Tsinghua-Baidu Scholarship, 2012
  • Scholarship for Tsinghua Xuetang Talents Program, 2011–2014
  • First Prize, National Senior High School Mathematical Olympiad, China, 2010
  • First Prize, National Olympiad in Informatics in Provinces, China, 2008
  • Organizer, ACM MMSys 2021 “Grand Challenge on Banwidth Estimation for Real-Time Communications”
  • Reviewer, IEEE/ACM Transactions on Networking (2019–)
  • Reviewer, ACM SIGCOMM Computer Communication Review (2019–)
  • Reviewer, Computer Communications (2019–)