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

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I was advised by Professor Dave Andersen and Michael Kaminsky for three years. In the NetSys Lab, I worked on big data analytics systems with Kay Ousterhout and Professors Sylvia Ratnasamy and Scott Shenker. 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