I am a Senior Researcher at
Microsoft Research Redmond
Mobility and Networking Research Group.
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 validating new algorithms.
I completed my Ph.D. in computer science at Stanford University in 2020, advised by
Keith Winstein and
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.
The two papers composing my dissertation received the
Community Award at USENIX NSDI 2020 and the
Best Paper Award at USENIX ATC 2018 respectively.
Before that, I graduated from Tsinghua University in 2015, 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 the spring of 2014.
I am continuously looking for students who are interested in applying ML/RL to
systems and networking research to work with me remotely and potentially intern at
Microsoft Research during the next summer. I would be happy to talk to you (and your
advisors) about possible projects to collaborate on.
Francis Y. Yan, Hudson Ayers, Chenzhi Zhu, Sadjad Fouladi, James Hong,
Keyi Zhang, Philip Levis, Keith Winstein
(“for the best paper whose code and/or data set
is made publicly available”)
USENIX Symposium on Networked Systems Design and Implementation (NSDI),
, 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
Best Paper Award
USENIX Annual Technical Conference (ATC), July 2018
is a community “training
ground” for Internet congestion-control research and has
assisted four schemes from other research groups in publishing at
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.