My research broadly covers high performance software systems, from datacenter congestion control to protocols for hybrid packet/circuit networks and efficient ML inference on resource-constrained devices.

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For Summer 2021, I am working on microburst congestion control with the Facebook Systems & Infrastructure, Host Networking team.

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In Fall 2021, I will be an instructor for CMU's 15-441/641 Networking and the Internet course. Feel free to reach out to me with questions about the course.

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Working with Professor Dave Andersen and Michael Kaminsky, I was a lead member of the Intel Science and Technology Center for Visual Cloud Systems. Previously, I built big data analytics systems with Kay Ousterhout and Professors Sylvia Ratnasamy and Scott Shenker in the NetSys Lab. I received my B.S. in Electrical Engineering and Computer Science from UC Berkeley in 2015.

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I am from San Diego, CA.

Publications   Google Scholar

2020

Adapting TCP for reconfigurable datacenter networks NSDI 2020

Matthew K. Mukerjee, Christopher Canel, Weiyang Wang, Daehyeok Kim, Srinivasan Seshan, Alex C. Snoeren

paper, bibtex, slides, video

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2019

Scaling video analytics on constrained edge nodes SysML 2019

Christopher Canel, Thomas Kim, Giulio Zhou, Conglong Li, Hyeontaek Lim, David G. Andersen, Michael Kaminsky, 

Subramanya R. Dulloor

paper, bibtex, extended paper, poster, slides, video

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2018

Mainstream: Dynamic stem-sharing for multi-tenant video processing USENIX ATC 2018

Angela Jiang, Daniel L.-K. Wong, Christopher Canel, Lilia Tang, Ishan Misra, Michael Kaminsky,

Michael A. Kozuch, Padmanabhan Pillai, David G. Andersen, Gregory R. Ganger

paper, bibtex, audio

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2017

Monotasks: Architecting for performance clarity in data analytics frameworks SOSP 2017

Kay Ousterhout, Christopher Canel, Sylvia Ratnasamy, Scott Shenker

paper, bibtex

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Performance clarity as a first-class design principle HotOS 2017

Kay Ousterhout, Christopher Canel, Max Wolffe, Sylvia Ratnasamy, Scott Shenker

paper, bibtex